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Jiang N, Zhu XG. Modern phenomics to empower holistic crop science, agronomy, and breeding research. J Genet Genomics 2024:S1673-8527(24)00102-4. [PMID: 38734136 DOI: 10.1016/j.jgg.2024.04.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 04/25/2024] [Accepted: 04/30/2024] [Indexed: 05/13/2024]
Abstract
Crop phenomics enables collection of diverse plant traits for a large number of samples along different time scales, representing a greater data collection throughput compared to the traditional measurements. Most of modern crop phenomics use different sensors to collect reflective, emitted and fluorescence signals etc., from plant organs at different spatial and temporal resolutions. Such multi-modal, high dimensional data not only accelerates basic research on crop physiology, genetics, and whole plant systems modeling, but also supports the optimization of field agronomic practices, internal environments of plant factories, and ultimately crop breeding. Major challenges and opportunities facing the current crop phenomics research community include developing community consensus or standards for data collection, management, sharing, and processing, developing capabilities to measure physiological parameters, and enabling farmers and breeders to effectively use phenomics in the field to directly support agricultural production.
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Affiliation(s)
- Ni Jiang
- Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.
| | - Xin-Guang Zhu
- Center of Excellence for Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai 200032, China.
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2
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Vurro F, Croci M, Impollonia G, Marchetti E, Gracia-Romero A, Bettelli M, Araus JL, Amaducci S, Janni M. Field Plant Monitoring from Macro to Micro Scale: Feasibility and Validation of Combined Field Monitoring Approaches from Remote to in Vivo to Cope with Drought Stress in Tomato. PLANTS (BASEL, SWITZERLAND) 2023; 12:3851. [PMID: 38005747 PMCID: PMC10674827 DOI: 10.3390/plants12223851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 10/10/2023] [Accepted: 10/18/2023] [Indexed: 11/26/2023]
Abstract
Monitoring plant growth and development during cultivation to optimize resource use efficiency is crucial to achieve an increased sustainability of agriculture systems and ensure food security. In this study, we compared field monitoring approaches from the macro to micro scale with the aim of developing novel in vivo tools for field phenotyping and advancing the efficiency of drought stress detection at the field level. To this end, we tested different methodologies in the monitoring of tomato growth under different water regimes: (i) micro-scale (inserted in the plant stem) real-time monitoring with an organic electrochemical transistor (OECT)-based sensor, namely a bioristor, that enables continuous monitoring of the plant; (ii) medium-scale (<1 m from the canopy) monitoring through red-green-blue (RGB) low-cost imaging; (iii) macro-scale multispectral and thermal monitoring using an unmanned aerial vehicle (UAV). High correlations between aerial and proximal remote sensing were found with chlorophyll-related indices, although at specific time points (NDVI and NDRE with GGA and SPAD). The ion concentration and allocation monitored by the index R of the bioristor during the drought defense response were highly correlated with the water use indices (Crop Water Stress Index (CSWI), relative water content (RWC), vapor pressure deficit (VPD)). A high negative correlation was observed with the CWSI and, in turn, with the RWC. Although proximal remote sensing measurements correlated well with water stress indices, vegetation indices provide information about the crop's status at a specific moment. Meanwhile, the bioristor continuously monitors the ion movements and the correlated water use during plant growth and development, making this tool a promising device for field monitoring.
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Affiliation(s)
- Filippo Vurro
- Istituto dei Materiali per l’Elettronica e il Magnetismo (IMEM-CNR), Parco Area delle Scienze 37/A, 43124 Parma, Italy; (F.V.); (M.B.)
| | - Michele Croci
- Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Via Emilia Parmense, 84, 29122 Piacenza, Italy; (M.C.); (S.A.)
| | - Giorgio Impollonia
- Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Via Emilia Parmense, 84, 29122 Piacenza, Italy; (M.C.); (S.A.)
| | - Edoardo Marchetti
- Istituto dei Materiali per l’Elettronica e il Magnetismo (IMEM-CNR), Parco Area delle Scienze 37/A, 43124 Parma, Italy; (F.V.); (M.B.)
| | - Adrian Gracia-Romero
- Integrative Crop Ecophysiology Group, Agrotecnio—Center for Research in Agrotechnology, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain; (A.G.-R.); (J.L.A.)
- Field Crops Program, Institute for Food and Agricultural Research and Technology (IRTA), 251981 Lleida, Spain
| | - Manuele Bettelli
- Istituto dei Materiali per l’Elettronica e il Magnetismo (IMEM-CNR), Parco Area delle Scienze 37/A, 43124 Parma, Italy; (F.V.); (M.B.)
| | - José Luis Araus
- Integrative Crop Ecophysiology Group, Agrotecnio—Center for Research in Agrotechnology, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain; (A.G.-R.); (J.L.A.)
| | - Stefano Amaducci
- Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Via Emilia Parmense, 84, 29122 Piacenza, Italy; (M.C.); (S.A.)
| | - Michela Janni
- Istituto dei Materiali per l’Elettronica e il Magnetismo (IMEM-CNR), Parco Area delle Scienze 37/A, 43124 Parma, Italy; (F.V.); (M.B.)
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Jing L, Wei X, Song Q, Wang F. Research on Estimating Rice Canopy Height and LAI Based on LiDAR Data. SENSORS (BASEL, SWITZERLAND) 2023; 23:8334. [PMID: 37837163 PMCID: PMC10575206 DOI: 10.3390/s23198334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 10/06/2023] [Accepted: 10/08/2023] [Indexed: 10/15/2023]
Abstract
Rice canopy height and density are directly usable crop phenotypic traits for the direct estimation of crop biomass. Therefore, it is crucial to rapidly and accurately estimate these phenotypic parameters. To achieve the non-destructive detection and estimation of these essential parameters in rice, a platform based on LiDAR (Light Detection and Ranging) point cloud data for rice phenotypic parameter detection was established. Data collection of rice canopy layers was performed across multiple plots. The LiDAR-detected canopy-top point clouds were selected using a method based on the highest percentile, and a surface model of the canopy was calculated. The canopy height estimation was the difference between the ground elevation and the percentile value. To determine the optimal percentile that would define the rice canopy top, testing was conducted incrementally at percentile values from 0.8 to 1, with increments of 0.005. The optimal percentile value was found to be 0.975. The root mean square error (RMSE) between the LiDAR-detected and manually measured canopy heights for each case was calculated. The prediction model based on canopy height (R2 = 0.941, RMSE = 0.019) exhibited a strong correlation with the actual canopy height. Linear regression analysis was conducted between the gap fractions of different plots, and the average rice canopy Leaf Area Index (LAI) was manually detected. Prediction models of canopy LAIs based on ground return counts (R2 = 0.24, RMSE = 0.1) and ground return intensity (R2 = 0.28, RMSE = 0.09) showed strong correlations but had lower correlations with rice canopy LAIs. Regression analysis was performed between LiDAR-detected canopy heights and manually measured rice canopy LAIs. The results thereof indicated that the prediction model based on canopy height (R2 = 0.77, RMSE = 0.03) was more accurate.
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Affiliation(s)
| | - Xinhua Wei
- Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education of the People’s Republic of China, Institute of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
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Cudjoe DK, Virlet N, Castle M, Riche AB, Mhada M, Waine TW, Mohareb F, Hawkesford MJ. Field phenotyping for African crops: overview and perspectives. FRONTIERS IN PLANT SCIENCE 2023; 14:1219673. [PMID: 37860243 PMCID: PMC10582954 DOI: 10.3389/fpls.2023.1219673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 09/07/2023] [Indexed: 10/21/2023]
Abstract
Improvements in crop productivity are required to meet the dietary demands of the rapidly-increasing African population. The development of key staple crop cultivars that are high-yielding and resilient to biotic and abiotic stresses is essential. To contribute to this objective, high-throughput plant phenotyping approaches are important enablers for the African plant science community to measure complex quantitative phenotypes and to establish the genetic basis of agriculturally relevant traits. These advances will facilitate the screening of germplasm for optimum performance and adaptation to low-input agriculture and resource-constrained environments. Increasing the capacity to investigate plant function and structure through non-invasive technologies is an effective strategy to aid plant breeding and additionally may contribute to precision agriculture. However, despite the significant global advances in basic knowledge and sensor technology for plant phenotyping, Africa still lags behind in the development and implementation of these systems due to several practical, financial, geographical and political barriers. Currently, field phenotyping is mostly carried out by manual methods that are prone to error, costly, labor-intensive and may come with adverse economic implications. Therefore, improvements in advanced field phenotyping capabilities and appropriate implementation are key factors for success in modern breeding and agricultural monitoring. In this review, we provide an overview of the current state of field phenotyping and the challenges limiting its implementation in some African countries. We suggest that the lack of appropriate field phenotyping infrastructures is impeding the development of improved crop cultivars and will have a detrimental impact on the agricultural sector and on food security. We highlight the prospects for integrating emerging and advanced low-cost phenotyping technologies into breeding protocols and characterizing crop responses to environmental challenges in field experimentation. Finally, we explore strategies for overcoming the barriers and maximizing the full potential of emerging field phenotyping technologies in African agriculture. This review paper will open new windows and provide new perspectives for breeders and the entire plant science community in Africa.
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Affiliation(s)
- Daniel K. Cudjoe
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
- School of Water, Energy and Environment, Cranfield University, Cranfield, Bedfordshire, United Kingdom
| | - Nicolas Virlet
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
| | - March Castle
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
| | - Andrew B. Riche
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
| | - Manal Mhada
- AgroBiosciences Department, Mohammed VI Polytechnic University (UM6P), Benguérir, Morocco
| | - Toby W. Waine
- School of Water, Energy and Environment, Cranfield University, Cranfield, Bedfordshire, United Kingdom
| | - Fady Mohareb
- School of Water, Energy and Environment, Cranfield University, Cranfield, Bedfordshire, United Kingdom
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Mathew J, Delavarpour N, Miranda C, Stenger J, Zhang Z, Aduteye J, Flores P. A Novel Approach to Pod Count Estimation Using a Depth Camera in Support of Soybean Breeding Applications. SENSORS (BASEL, SWITZERLAND) 2023; 23:6506. [PMID: 37514799 PMCID: PMC10384073 DOI: 10.3390/s23146506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 06/28/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023]
Abstract
Improving soybean (Glycine max L. (Merr.)) yield is crucial for strengthening national food security. Predicting soybean yield is essential to maximize the potential of crop varieties. Non-destructive methods are needed to estimate yield before crop maturity. Various approaches, including the pod-count method, have been used to predict soybean yield, but they often face issues with the crop background color. To address this challenge, we explored the application of a depth camera to real-time filtering of RGB images, aiming to enhance the performance of the pod-counting classification model. Additionally, this study aimed to compare object detection models (YOLOV7 and YOLOv7-E6E) and select the most suitable deep learning (DL) model for counting soybean pods. After identifying the best architecture, we conducted a comparative analysis of the model's performance by training the DL model with and without background removal from images. Results demonstrated that removing the background using a depth camera improved YOLOv7's pod detection performance by 10.2% precision, 16.4% recall, 13.8% mAP@50, and 17.7% mAP@0.5:0.95 score compared to when the background was present. Using a depth camera and the YOLOv7 algorithm for pod detection and counting yielded a mAP@0.5 of 93.4% and mAP@0.5:0.95 of 83.9%. These results indicated a significant improvement in the DL model's performance when the background was segmented, and a reasonably larger dataset was used to train YOLOv7.
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Affiliation(s)
- Jithin Mathew
- Agricultural and Biosystems Engineering Department, North Dakota State University, Fargo, ND 58105, USA
| | - Nadia Delavarpour
- Agricultural and Biosystems Engineering Department, North Dakota State University, Fargo, ND 58105, USA
| | - Carrie Miranda
- Department of Plant Sciences, North Dakota State University, Fargo, ND 58105, USA
| | - John Stenger
- North Dakota Agricultural Weather Network, School of Natural Resource Sciences, North Dakota State University, Fargo, ND 58105, USA
| | - Zhao Zhang
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
| | - Justice Aduteye
- Department of Agronomy, Earth University, San Jose 4442-1000, Costa Rica
| | - Paulo Flores
- Agricultural and Biosystems Engineering Department, North Dakota State University, Fargo, ND 58105, USA
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Sun W, He Q, Liu J, Xiao X, Wu Y, Zhou S, Ma S, Wang R. Dynamic monitoring of maize grain quality based on remote sensing data. FRONTIERS IN PLANT SCIENCE 2023; 14:1177477. [PMID: 37426960 PMCID: PMC10325687 DOI: 10.3389/fpls.2023.1177477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 05/31/2023] [Indexed: 07/11/2023]
Abstract
Remote sensing data have been widely used to monitor crop development, grain yield, and quality, while precise monitoring of quality traits, especially grain starch and oil contents considering meteorological elements, still needs to be improved. In this study, the field experiment with different sowing time, i.e., 8 June, 18 June, 28 June, and 8 July, was conducted in 2018-2020. The scalable annual and inter-annual quality prediction model for summer maize in different growth periods was established using hierarchical linear modeling (HLM), which combined hyperspectral and meteorological data. Compared with the multiple linear regression (MLR) using vegetation indices (VIs), the prediction accuracy of HLM was obviously improved with the highest R 2, root mean square error (RMSE), and mean absolute error (MAE) values of 0.90, 0.10, and 0.08, respectively (grain starch content (GSC)); 0.87, 0.10, and 0.08, respectively (grain protein content (GPC)); and 0.74, 0.13, and 0.10, respectively (grain oil content (GOC)). In addition, the combination of the tasseling, grain-filling, and maturity stages further improved the predictive power for GSC (R 2 = 0.96). The combination of the grain-filling and maturity stages further improved the predictive power for GPC (R 2 = 0.90). The prediction accuracy developed in the combination of the jointing and tasseling stages for GOC (R 2 = 0.85). The results also showed that meteorological factors, especially precipitation, had a great influence on grain quality monitoring. Our study provided a new idea for crop quality monitoring by remote sensing.
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Affiliation(s)
- Weiwei Sun
- College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
| | - Qijin He
- College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing, China
| | - Jiahong Liu
- College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
| | - Xiao Xiao
- College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
| | - Yaxin Wu
- College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
| | - Sijia Zhou
- College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
| | - Selimai Ma
- College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
| | - Rongwan Wang
- College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
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Tolley SA, Carpenter N, Crawford MM, Delp EJ, Habib A, Tuinstra MR. Row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modeling. FRONTIERS IN PLANT SCIENCE 2023; 14:1202536. [PMID: 37409309 PMCID: PMC10318590 DOI: 10.3389/fpls.2023.1202536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 06/06/2023] [Indexed: 07/07/2023]
Abstract
Remote sensing enables the rapid assessment of many traits that provide valuable information to plant breeders throughout the growing season to improve genetic gain. These traits are often extracted from remote sensing data on a row segment (rows within a plot) basis enabling the quantitative assessment of any row-wise subset of plants in a plot, rather than a few individual representative plants, as is commonly done in field-based phenotyping. Nevertheless, which rows to include in analysis is still a matter of debate. The objective of this experiment was to evaluate row selection and plot trimming in field trials conducted using four-row plots with remote sensing traits extracted from RGB (red-green-blue), LiDAR (light detection and ranging), and VNIR (visible near infrared) hyperspectral data. Uncrewed aerial vehicle flights were conducted throughout the growing seasons of 2018 to 2021 with data collected on three years of a sorghum experiment and two years of a maize experiment. Traits were extracted from each plot based on all four row segments (RS) (RS1234), inner rows (RS23), outer rows (RS14), and individual rows (RS1, RS2, RS3, and RS4). Plot end trimming of 40 cm was an additional factor tested. Repeatability and predictive modeling of end-season yield were used to evaluate performance of these methodologies. Plot trimming was never shown to result in significantly different outcomes from non-trimmed plots. Significant differences were often observed based on differences in row selection. Plots with more row segments were often favorable for increasing repeatability, and excluding outer rows improved predictive modeling. These results support long-standing principles of experimental design in agronomy and should be considered in breeding programs that incorporate remote sensing.
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Affiliation(s)
- Seth A. Tolley
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Neal Carpenter
- Analytics and Pipeline Design, Bayer Crop Science, Chesterfield, MO, United States
| | - Melba M. Crawford
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
- Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, United States
| | - Edward J. Delp
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States
| | - Ayman Habib
- Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, United States
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Li X, Chen Z, Wei X, Zhao T, Jin J. Development of a Target-to-Sensor Mode Multispectral Imaging Device for High-Throughput and High-Precision Touch-Based Leaf-Scale Soybean Phenotyping. SENSORS (BASEL, SWITZERLAND) 2023; 23:3756. [PMID: 37050815 PMCID: PMC10098662 DOI: 10.3390/s23073756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 03/30/2023] [Accepted: 04/03/2023] [Indexed: 06/19/2023]
Abstract
Image-based spectroscopy phenotyping is a rapidly growing field that investigates how genotype, environment and management interact using remote or proximal sensing systems to capture images of a plant under multiple wavelengths of light. While remote sensing techniques have proven effective in crop phenotyping, they can be subject to various noise sources, such as varying lighting conditions and plant physiological status, including leaf orientation. Moreover, current proximal leaf-scale imaging devices require the sensors to accommodate the state of the samples during imaging which induced extra time and labor cost. Therefore, this study developed a proximal multispectral imaging device that can actively attract the leaf to the sensing area (target-to-sensor mode) for high-precision and high-throughput leaf-scale phenotyping. To increase the throughput and to optimize imaging results, this device innovatively uses active airflow to reposition and flatten the soybean leaf. This novel mechanism redefines the traditional sensor-to-target mode and has relieved the device operator from the labor of capturing and holding the leaf, resulting in a five-fold increase in imaging speed compared to conventional proximal whole leaf imaging device. Besides, this device uses artificial lights to create stable and consistent lighting conditions to further improve the quality of the images. Furthermore, the touch-based imaging device takes full advantage of proximal sensing by providing ultra-high spatial resolution and quality of each pixel by blocking the noises induced by ambient lighting variances. The images captured by this device have been tested in the field and proven effective. Specifically, it has successfully identified nitrogen deficiency treatment at an earlier stage than a typical remote sensing system. The p-value of the data collected by the device (p = 0.008) is significantly lower than that of a remote sensing system (p = 0.239).
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Oteng-Frimpong R, Karikari B, Sie EK, Kassim YB, Puozaa DK, Rasheed MA, Fonceka D, Okello DK, Balota M, Burow M, Ozias-Akins P. Multi-locus genome-wide association studies reveal genomic regions and putative candidate genes associated with leaf spot diseases in African groundnut ( Arachis hypogaea L.) germplasm. FRONTIERS IN PLANT SCIENCE 2023; 13:1076744. [PMID: 36684745 PMCID: PMC9849250 DOI: 10.3389/fpls.2022.1076744] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
Early leaf spot (ELS) and late leaf spot (LLS) diseases are the two most destructive groundnut diseases in Ghana resulting in ≤ 70% yield losses which is controlled largely by chemical method. To develop leaf spot resistant varieties, the present study was undertaken to identify single nucleotide polymorphism (SNP) markers and putative candidate genes underlying both ELS and LLS. In this study, six multi-locus models of genome-wide association study were conducted with the best linear unbiased predictor obtained from 294 African groundnut germplasm screened for ELS and LLS as well as image-based indices of leaf spot diseases severity in 2020 and 2021 and 8,772 high-quality SNPs from a 48 K SNP array Axiom platform. Ninety-seven SNPs associated with ELS, LLS and five image-based indices across the chromosomes in the 2 two sub-genomes. From these, twenty-nine unique SNPs were detected by at least two models for one or more traits across 16 chromosomes with explained phenotypic variation ranging from 0.01 - 62.76%, with exception of chromosome (Chr) 08 (Chr08), Chr10, Chr11, and Chr19. Seventeen potential candidate genes were predicted at ± 300 kbp of the stable/prominent SNP positions (12 and 5, down- and upstream, respectively). The results from this study provide a basis for understanding the genetic architecture of ELS and LLS diseases in African groundnut germplasm, and the associated SNPs and predicted candidate genes would be valuable for breeding leaf spot diseases resistant varieties upon further validation.
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Affiliation(s)
- Richard Oteng-Frimpong
- Groundnut Improvement Program, Council for Scientific and Industrial Research (CSIR)-Savanna Agricultural Research Institute, Tamale, Ghana
| | - Benjamin Karikari
- Department of Agricultural Biotechnology, Faculty of Agriculture, Food and Consumer Sciences, University for Development Studies, Tamale, Ghana
| | - Emmanuel Kofi Sie
- Groundnut Improvement Program, Council for Scientific and Industrial Research (CSIR)-Savanna Agricultural Research Institute, Tamale, Ghana
| | - Yussif Baba Kassim
- Groundnut Improvement Program, Council for Scientific and Industrial Research (CSIR)-Savanna Agricultural Research Institute, Tamale, Ghana
| | - Doris Kanvenaa Puozaa
- Groundnut Improvement Program, Council for Scientific and Industrial Research (CSIR)-Savanna Agricultural Research Institute, Tamale, Ghana
| | - Masawudu Abdul Rasheed
- Groundnut Improvement Program, Council for Scientific and Industrial Research (CSIR)-Savanna Agricultural Research Institute, Tamale, Ghana
| | - Daniel Fonceka
- Centre d’Etude Régional pour l’Amélioration de l’Adaptation àla Sécheresse (CERAAS), Institut Sénégalais de Recherches Agricoles (ISRA), Thiès, Senegal
| | - David Kallule Okello
- Oil Crops Research Program, National Semi-Arid Resources Research Institute (NaSARRI), Soroti, Uganda
| | - Maria Balota
- School of Plant and Environmental Sciences, Tidewater Agricultural Research and Extension Center (AREC), Virginia Tech, Suffolk, VA, United States
| | - Mark Burow
- Texas A&M AgriLife Research and Department of Plant and Soil Science, Texas Tech University, Lubbock, TX, United States
| | - Peggy Ozias-Akins
- Institute of Plant Breeding Genetics and Genomics, University of Georgia, Tifton, GA, United States
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Genetic trends in CIMMYT's tropical maize breeding pipelines. Sci Rep 2022; 12:20110. [PMID: 36418412 PMCID: PMC9684471 DOI: 10.1038/s41598-022-24536-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 11/16/2022] [Indexed: 11/24/2022] Open
Abstract
Fostering a culture of continuous improvement through regular monitoring of genetic trends in breeding pipelines is essential to improve efficiency and increase accountability. This is the first global study to estimate genetic trends across the International Maize and Wheat Improvement Center (CIMMYT) tropical maize breeding pipelines in eastern and southern Africa (ESA), South Asia, and Latin America over the past decade. Data from a total of 4152 advanced breeding trials and 34,813 entries, conducted at 1331 locations in 28 countries globally, were used for this study. Genetic trends for grain yield reached up to 138 kg ha-1 yr-1 in ESA, 118 kg ha-1 yr-1 South Asia and 143 kg ha-1 yr-1 in Latin America. Genetic trend was, in part, related to the extent of deployment of new breeding tools in each pipeline, strength of an extensive phenotyping network, and funding stability. Over the past decade, CIMMYT's breeding pipelines have significantly evolved, incorporating new tools/technologies to increase selection accuracy and intensity, while reducing cycle time. The first pipeline, Eastern Africa Product Profile 1a (EA-PP1a), to implement marker-assisted forward-breeding for resistance to key diseases, coupled with rapid-cycle genomic selection for drought, recorded a genetic trend of 2.46% per year highlighting the potential for deploying new tools/technologies to increase genetic gain.
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Pipatsitee P, Tisarum R, Taota K, Samphumphuang T, Eiumnoh A, Singh HP, Cha-Um S. Effectiveness of vegetation indices and UAV-multispectral imageries in assessing the response of hybrid maize (Zea mays L.) to water deficit stress under field environment. ENVIRONMENTAL MONITORING AND ASSESSMENT 2022; 195:128. [PMID: 36402920 DOI: 10.1007/s10661-022-10766-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 11/12/2022] [Indexed: 06/16/2023]
Abstract
Unmanned aerial vehicles (UAVs) equipped with multi-sensors are one of the most innovative technologies for measuring plant health and predicting final yield in field conditions, especially in the water deficit situation in rain-deprived regions. The objective of this investigation was to evaluate the individual plant and canopy-level measurements using UAV imageries in three different genotypes, Suwan4452 (drought-tolerant), Pac339, and S7328 (drought-sensitive) of maize (Zea mays L.) at vegetative and reproductive stages under WW (well-watered) and WD (water deficit) conditions. At the vegetative stage, only CWSI (crop water stress index) of Pac339 and S7328 under WD increased significantly by 1.86- and 1.69-fold over WW, whereas the vegetation indices (EVI2 (Enhanced Vegetation Index 2), OSAVI (Optimized Soil-Adjusted Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), NDRE (Normalized Difference Red Edge Index), and NDVI (Normalized Difference Vegetation Index)) derived from UAV multi-sensors did not vary. At the reproductive stage, CWSI in drought-sensitive genotype (S7328) under WD increased by 1.92-fold over WW. All the vegetation indices (EVI2, OSAVI, GNDVI, NDRE, and NDVI) of Pac339 and S7328 under WD decreased when compared with those of Suwan4452. NDVI derived from GreenSeeker® handheld and NDVI from UAV data was closely related (R2 = 0.5924). An increase in leaf temperature (Tleaf) and reduction in NDVI of WD stressed maize plants was observed (R2 = 0.5829) leading to yield loss (R2 = 0.5198). In summary, a close correlation was observed between the physiological data of individual plants and vegetation indices of canopy level (collected using a UAV platform) in drought-sensitive genotypes of maize crops under WD conditions, thus indicating its effectiveness in the classification of drought-tolerant genotypes.
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Affiliation(s)
- Piyanan Pipatsitee
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), 113 Thailand Science Park, Paholyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani, 12120, Thailand
| | - Rujira Tisarum
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), 113 Thailand Science Park, Paholyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani, 12120, Thailand
| | - Kanyarat Taota
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), 113 Thailand Science Park, Paholyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani, 12120, Thailand
| | - Thapanee Samphumphuang
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), 113 Thailand Science Park, Paholyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani, 12120, Thailand
| | - Apisit Eiumnoh
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), 113 Thailand Science Park, Paholyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani, 12120, Thailand
| | - Harminder Pal Singh
- Department of Environment Studies, Faculty of Science, Panjab University, Chandigarh, 160014, India
| | - Suriyan Cha-Um
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), 113 Thailand Science Park, Paholyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani, 12120, Thailand.
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Tao H, Xu S, Tian Y, Li Z, Ge Y, Zhang J, Wang Y, Zhou G, Deng X, Zhang Z, Ding Y, Jiang D, Guo Q, Jin S. Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives. PLANT COMMUNICATIONS 2022; 3:100344. [PMID: 35655429 PMCID: PMC9700174 DOI: 10.1016/j.xplc.2022.100344] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 05/08/2022] [Accepted: 05/27/2022] [Indexed: 06/01/2023]
Abstract
Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of genomics and environment on plants, limiting the progress of smart breeding and precise cultivation. High-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of traits. Proximal and remote sensing (PRS) techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and analysis. Substantial progress of PRS applications in PP has been observed over the last two decades and is analyzed here from an interdisciplinary perspective based on 2972 publications. This progress covers most aspects of PRS application in PP, including patterns of global spatial distribution and temporal dynamics, specific PRS technologies, phenotypic research fields, working environments, species, and traits. Subsequently, we demonstrate how to link PRS to multi-omics studies, including how to achieve multi-dimensional PRS data acquisition and processing, how to systematically integrate all kinds of phenotypic information and derive phenotypic knowledge with biological significance, and how to link PP to multi-omics association analysis. Finally, we identify three future perspectives for PRS-based PP: (1) strengthening the spatial and temporal consistency of PRS data, (2) exploring novel phenotypic traits, and (3) facilitating multi-omics communication.
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Affiliation(s)
- Haiyu Tao
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Shan Xu
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Yongchao Tian
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Zhaofeng Li
- The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Agriculture College, Shihezi University, Shihezi 832003, China
| | - Yan Ge
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Jiaoping Zhang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Nanjing Agricultural University, Nanjing 210095, China
| | - Yu Wang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Guodong Zhou
- Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Xiong Deng
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Ze Zhang
- The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Agriculture College, Shihezi University, Shihezi 832003, China
| | - Yanfeng Ding
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Dong Jiang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Qinghua Guo
- Institute of Ecology, College of Urban and Environmental Science, Peking University, Beijing 100871, China
| | - Shichao Jin
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China; Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Sciences, Nanjing University, Nanjing, Jiangsu 210023, China.
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Automatic non-destructive multiple lettuce traits prediction based on DeepLabV3 +. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2022. [DOI: 10.1007/s11694-022-01660-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Fu YX, Liu SY, Guo WY, Dong J, Nan JX, Lin HY, Mei LC, Yang WC, Yang GF. In vivo diagnostics of abiotic plant stress responses via in situ real-time fluorescence imaging. PLANT PHYSIOLOGY 2022; 190:196-201. [PMID: 35670737 PMCID: PMC9434263 DOI: 10.1093/plphys/kiac273] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 05/13/2022] [Indexed: 06/09/2023]
Abstract
A hydroxyphenylpyruvate dioxygenase-targeted fluorescent biosensor enables the early diagnostics of abiotic stresses in plants.
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Affiliation(s)
| | | | | | - Jin Dong
- Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, International Joint Research Center for Intelligent Biosensor Technology and Health, College of Chemistry, Central China Normal University, Wuhan 430079, P.R. China
| | - Jia-Xu Nan
- Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, International Joint Research Center for Intelligent Biosensor Technology and Health, College of Chemistry, Central China Normal University, Wuhan 430079, P.R. China
| | - Hong-Yan Lin
- Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, International Joint Research Center for Intelligent Biosensor Technology and Health, College of Chemistry, Central China Normal University, Wuhan 430079, P.R. China
| | - Long-Can Mei
- Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, International Joint Research Center for Intelligent Biosensor Technology and Health, College of Chemistry, Central China Normal University, Wuhan 430079, P.R. China
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Zhang Y, Zhao D, Liu H, Huang X, Deng J, Jia R, He X, Tahir MN, Lan Y. Research hotspots and frontiers in agricultural multispectral technology: Bibliometrics and scientometrics analysis of the Web of Science. FRONTIERS IN PLANT SCIENCE 2022; 13:955340. [PMID: 36035687 PMCID: PMC9404299 DOI: 10.3389/fpls.2022.955340] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Accepted: 07/08/2022] [Indexed: 06/15/2023]
Abstract
Multispectral technology has a wide range of applications in agriculture. By obtaining spectral information during crop production, key information such as growth, pests and diseases, fertilizer and pesticide application can be determined quickly, accurately and efficiently. The scientific analysis based on Web of Science aims to understand the research hotspots and areas of interest in the field of agricultural multispectral technology. The publications related to agricultural multispectral research in agriculture between 2002 and 2021 were selected as the research objects. The softwares of CiteSpace, VOSviewer, and Microsoft Excel were used to provide a comprehensive review of agricultural multispectral research in terms of research areas, institutions, influential journals, and core authors. Results of the analysis show that the number of publications increased each year, with the largest increase in 2019. Remote sensing, imaging technology, environmental science, and ecology are the most popular research directions. The journal Remote Sensing is one of the most popular publishers, showing a high publishing potential in multispectral research in agriculture. The institution with the most research literature and citations is the USDA. In terms of the number of papers, Mtanga is the author with the most published articles in recent years. Through keyword co-citation analysis, it is determined that the main research areas of this topic focus on remote sensing, crop classification, plant phenotypes and other research areas. The literature co-citation analysis indicates that the main research directions concentrate in vegetation index, satellite remote sensing applications and machine learning modeling. There is still a lot of room for development of multi-spectrum technology. Further development can be carried out in the areas of multi-device synergy, spectral fusion, airborne equipment improvement, and real-time image processing technology, which will cooperate with each other to further play the role of multi-spectrum in agriculture and promote the development of agriculture.
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Affiliation(s)
- Yali Zhang
- College of Engineering, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou, China
| | - Dehua Zhao
- College of Engineering, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou, China
| | - Hanchao Liu
- College of Engineering, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou, China
| | - Xinrong Huang
- College of Engineering, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou, China
| | - Jizhong Deng
- College of Engineering, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou, China
| | - Ruichang Jia
- College of Engineering, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou, China
| | - Xiaoping He
- Department of Information Consulting, Library, South China Agricultural University, Guangzhou, China
| | - Muhammad Naveed Tahir
- Department of Agronomy, Pir Mehr Ali Shah-Arid Agriculture University, Rawalpindi, Pakistan
| | - Yubin Lan
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou, China
- College of Electronic Engineering and College of Artificial Intelligence, South China Agricultural University, Guangzhou, China
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Tayade R, Yoon J, Lay L, Khan AL, Yoon Y, Kim Y. Utilization of Spectral Indices for High-Throughput Phenotyping. PLANTS (BASEL, SWITZERLAND) 2022; 11:1712. [PMID: 35807664 PMCID: PMC9268975 DOI: 10.3390/plants11131712] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 06/20/2022] [Accepted: 06/24/2022] [Indexed: 06/15/2023]
Abstract
The conventional plant breeding evaluation of large sets of plant phenotypes with precision and speed is very challenging. Thus, consistent, automated, multifaceted, and high-throughput phenotyping (HTP) technologies are becoming increasingly significant as tools to aid conventional breeding programs to develop genetically improved crops. With rapid technological advancement, various vegetation indices (VIs) have been developed. These VI-based imaging approaches, linked with artificial intelligence and a variety of remote sensing applications, provide high-throughput evaluations, particularly in the field of precision agriculture. VIs can be used to analyze and predict different quantitative and qualitative aspects of vegetation. Here, we provide an overview of the various VIs used in agricultural research, focusing on those that are often employed for crop or vegetation evaluation, because that has a linear relationship to crop output, which is frequently utilized in crop chlorophyll, health, moisture, and production predictions. In addition, the following aspects are here described: the importance of VIs in crop research and precision agriculture, their utilization in HTP, recent photogrammetry technology, mapping, and geographic information system software integrated with unmanned aerial vehicles and its key features. Finally, we discuss the challenges and future perspectives of HTP technologies and propose approaches for the development of new tools to assess plants' agronomic traits and data-driven HTP resolutions for precision breeding.
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Affiliation(s)
- Rupesh Tayade
- Department of Applied Biosciences, Kyungpook National University, Daegu 41566, Korea; (R.T.); (L.L.)
| | - Jungbeom Yoon
- Horticultural and Herbal Crop Environment Division, National Institute of Horticultural and Herbal Science, Rural Development Administration, Wanju 55365, Korea;
| | - Liny Lay
- Department of Applied Biosciences, Kyungpook National University, Daegu 41566, Korea; (R.T.); (L.L.)
| | - Abdul Latif Khan
- Department of Engineering Technology, University of Houston, Texas, TX 77204, USA;
| | - Youngnam Yoon
- Crop Production Technology Research Division, National Institute of Crop Science, Rural Development Administration, Miryang 50424, Korea
| | - Yoonha Kim
- Department of Applied Biosciences, Kyungpook National University, Daegu 41566, Korea; (R.T.); (L.L.)
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Chapu I, Okello DK, Okello RCO, Odong TL, Sarkar S, Balota M. Exploration of Alternative Approaches to Phenotyping of Late Leaf Spot and Groundnut Rosette Virus Disease for Groundnut Breeding. FRONTIERS IN PLANT SCIENCE 2022; 13:912332. [PMID: 35774822 PMCID: PMC9238324 DOI: 10.3389/fpls.2022.912332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 05/06/2022] [Indexed: 06/15/2023]
Abstract
Late leaf spot (LLS), caused by Nothopassalora personata (Berk. & M.A Curt.), and groundnut rosette disease (GRD), [caused by groundnut rosette virus (GRV)], represent the most important biotic constraints to groundnut production in Uganda. Application of visual scores in selection for disease resistance presents a challenge especially when breeding experiments are large because it is resource-intensive, subjective, and error-prone. High-throughput phenotyping (HTP) can alleviate these constraints. The objective of this study is to determine if HTP derived indices can replace visual scores in a groundnut breeding program in Uganda. Fifty genotypes were planted under rain-fed conditions at two locations, Nakabango (GRD hotspot) and NaSARRI (LLS hotspot). Three handheld sensors (RGB camera, GreenSeeker, and Thermal camera) were used to collect HTP data on the dates visual scores were taken. Pearson correlation was made between the indices and visual scores, and logistic models for predicting visual scores were developed. Normalized difference vegetation index (NDVI) (r = -0.89) and red-green-blue (RGB) color space indices CSI (r = 0.76), v* (r = -0.80), and b* (r = -0.75) were highly correlated with LLS visual scores. NDVI (r = -0.72), v* (r = -0.71), b* (r = -0.64), and GA (r = -0.67) were best related to the GRD visual symptoms. Heritability estimates indicated NDVI, green area (GA), greener area (GGA), a*, and hue angle having the highest heritability (H 2 > 0.75). Logistic models developed using these indices were 68% accurate for LLS and 45% accurate for GRD. The accuracy of the models improved to 91 and 84% when the nearest score method was used for LLS and GRD, respectively. Results presented in this study indicated that use of handheld remote sensing tools can improve screening for GRD and LLS resistance, and the best associated indices can be used for indirect selection for resistance and improve genetic gain in groundnut breeding.
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Affiliation(s)
- Ivan Chapu
- College of Agricultural and Environmental Sciences, Makerere University, Kampala, Uganda
| | | | - Robert C. Ongom Okello
- College of Agricultural and Environmental Sciences, Makerere University, Kampala, Uganda
| | - Thomas Lapaka Odong
- College of Agricultural and Environmental Sciences, Makerere University, Kampala, Uganda
| | - Sayantan Sarkar
- Blackland Research and Extension Center, Texas A&M AgriLife Research, Temple, TX, United States
| | - Maria Balota
- School of Plant and Environmental Sciences, Tidewater AREC, Virginia Tech, Suffolk, VA, United States
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18
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Gill T, Gill SK, Saini DK, Chopra Y, de Koff JP, Sandhu KS. A Comprehensive Review of High Throughput Phenotyping and Machine Learning for Plant Stress Phenotyping. PHENOMICS (CHAM, SWITZERLAND) 2022; 2:156-183. [PMID: 36939773 PMCID: PMC9590503 DOI: 10.1007/s43657-022-00048-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 01/29/2022] [Accepted: 02/11/2022] [Indexed: 02/04/2023]
Abstract
During the last decade, there has been rapid adoption of ground and aerial platforms with multiple sensors for phenotyping various biotic and abiotic stresses throughout the developmental stages of the crop plant. High throughput phenotyping (HTP) involves the application of these tools to phenotype the plants and can vary from ground-based imaging to aerial phenotyping to remote sensing. Adoption of these HTP tools has tried to reduce the phenotyping bottleneck in breeding programs and help to increase the pace of genetic gain. More specifically, several root phenotyping tools are discussed to study the plant's hidden half and an area long neglected. However, the use of these HTP technologies produces big data sets that impede the inference from those datasets. Machine learning and deep learning provide an alternative opportunity for the extraction of useful information for making conclusions. These are interdisciplinary approaches for data analysis using probability, statistics, classification, regression, decision theory, data visualization, and neural networks to relate information extracted with the phenotypes obtained. These techniques use feature extraction, identification, classification, and prediction criteria to identify pertinent data for use in plant breeding and pathology activities. This review focuses on the recent findings where machine learning and deep learning approaches have been used for plant stress phenotyping with data being collected using various HTP platforms. We have provided a comprehensive overview of different machine learning and deep learning tools available with their potential advantages and pitfalls. Overall, this review provides an avenue for studying various HTP platforms with particular emphasis on using the machine learning and deep learning tools for drawing legitimate conclusions. Finally, we propose the conceptual challenges being faced and provide insights on future perspectives for managing those issues.
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Affiliation(s)
- Taqdeer Gill
- grid.280741.80000 0001 2284 9820Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville, TN 37209 USA
| | - Simranveer K. Gill
- grid.412577.20000 0001 2176 2352College of Agriculture, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Dinesh K. Saini
- grid.412577.20000 0001 2176 2352Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Yuvraj Chopra
- grid.412577.20000 0001 2176 2352College of Agriculture, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Jason P. de Koff
- grid.280741.80000 0001 2284 9820Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville, TN 37209 USA
| | - Karansher S. Sandhu
- grid.30064.310000 0001 2157 6568Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99163 USA
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Akhtar R, Masud MM. Dynamic linkages between climatic variables and agriculture production in Malaysia: a generalized method of moments approach. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:41557-41566. [PMID: 35094275 DOI: 10.1007/s11356-021-18210-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 12/14/2021] [Indexed: 05/25/2023]
Abstract
Climate change continues to pose a threat to the agricultural sectors worldwide, jeopardizing food and nutritional security, which is a critical component of the sustainable development agenda. Consequently, this study attempts to examine the impact of climatic variables (CO2 emissions, energy resources, rainfall, temperature, fossil fuel consumption, and humidity) on agricultural production of rice, cereals, vegetables, coffee, and agriculture value added (as a percentage of GDP) in the Malaysian context. To this end, this study applied a generalized method of moments (GMM) estimator on the data obtained from the metrological station Malaysia, Department of Statistics Malaysia and World Development Indicators (WDI) spanning the period 1985-2016. The results revealed that temperature and energy consumption negatively and significantly affect rice and vegetable production, while the negative effect of rainfall, temperature, fossil fuel consumption, and humidity on cereal production is insignificant. The results also confirmed that CO2 emissions have a negative and significant impact on coffee production. Likewise, temperature, energy consumption, and fossil fuel consumption exhibit a negative and significant influence on agriculture value added. These observations evidenced the adverse effect of climate change on various agricultural products in Malaysia. Therefore, in order to ensure robust and sustainable agricultural output in Malaysia, policymakers as well as environmentalists should work together to formulate appropriate adaptation strategies.
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Affiliation(s)
- Rulia Akhtar
- Ungku Aziz Centre for Development Studies,, Office of Deputy Vice Chancellor (Research & Innovation), Universiti Malaya, Kuala Lumpur, Malaysia
| | - Muhammad Mehedi Masud
- Department of Development Studies, Faculty of Business and Economics, Universiti Malaya, Kuala Lumpur, Malaysia.
- Department of Business Administration, Daffodil International University, Dhaka, Bangladesh.
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Wang J, Bretz M, Dewan MAA, Delavar MA. Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 822:153559. [PMID: 35114222 DOI: 10.1016/j.scitotenv.2022.153559] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 01/20/2022] [Accepted: 01/26/2022] [Indexed: 06/14/2023]
Abstract
Land-use and land-cover change (LULCC) are of importance in natural resource management, environmental modelling and assessment, and agricultural production management. However, LULCC detection and modelling is a complex, data-driven process in the remote sensing field due to the processing of massive historical and current data, real-time interaction of scenario data, and spatial environmental data. In this paper, we review principles and methods of LULCC modelling, using machine learning and beyond, such as traditional cellular automata (CA). Then, we examine the characteristics, capabilities, limitations, and perspectives of machine learning. Machine learning has not yet been dramatic in modelling LULCC, such as urbanization prediction and crop yield prediction because competition and transition between land cover types are dynamic at a local scale under varying natural drivers and human activities. Upcoming challenges of machine learning in modelling LULCC remain in the detection and prediction of LULC evolutionary processes if considering their applicability and feasibility, such as the spatio-temporal transition mechanisms to describe occurrence, transition, spreading, and spatial patterns of changes, availability of training data of all the change drivers, particularly sequence data, and identification and inclusion of local ecological, hydrological, and social-economic drivers in addressing the spectral feature change. This review points out the need for multidisciplinary research beyond image processing and pattern recognition of machine learning in accelerating and advancing studies of LULCC modelling. Despite this, we believe that machine learning has strong potentials to incorporate new exploratory variables in modelling LULCC through expanding remote sensing big data and advancing transient algorithms.
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Affiliation(s)
- Junye Wang
- School of Computing & Information Systems, Faculty of Science and Technology, Canada; Center for Science, Faculty of Science and Technology, Athabasca University, 10011, 109 Street, Edmonton, AB T5J 3S8, Canada.
| | - Michael Bretz
- School of Computing & Information Systems, Faculty of Science and Technology, Canada
| | - M Ali Akber Dewan
- School of Computing & Information Systems, Faculty of Science and Technology, Canada
| | - Mojtaba Aghajani Delavar
- Center for Science, Faculty of Science and Technology, Athabasca University, 10011, 109 Street, Edmonton, AB T5J 3S8, Canada
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Szabó A, Mousavi SMN, Bojtor C, Ragán P, Nagy J, Vad A, Illés Á. Analysis of Nutrient-Specific Response of Maize Hybrids in Relation to Leaf Area Index (LAI) and Remote Sensing. PLANTS (BASEL, SWITZERLAND) 2022; 11:1197. [PMID: 35567198 PMCID: PMC9102345 DOI: 10.3390/plants11091197] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 04/23/2022] [Accepted: 04/26/2022] [Indexed: 06/15/2023]
Abstract
Leaf area index (LAI) indicates the leaf area per ground surface area occupied by a crop. Various methods are used to measure LAI, which is unitless and varies according to species and environmental conditions. This experiment was carried out in three different nitrogen ranges (control, 120 kg N ha-1, and 300 kg N ha-1) + PK nutrient levels, with five replications used for leaf area measurement on seven different maize hybrids. Hybrids had different moisture, protein, oil, and starch contents. N (1, 2) + PK treatments had a desirable effect on protein, starch, and yield. P0217 LAI had a minimal response at these fertiliser levels. LAI for Sushi peaked at different dates between control and fertiliser treatments. This result showed that Sushi has an excellent capacity for LAI. LAI values on 15 June 2020 showed minimum average values for all hybrids, and it had a maximum average values on 23 July 2020. LAI had maximum performance between the average values treatments in Sushi, Armagnac, Loupiac, and DKC4792 on 15 June 2020. This study also provides insights for examining variably applied N doses using crop sensors and UAV remote-sensing platforms.
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Affiliation(s)
- Atala Szabó
- Faculty of Agricultural and Food Sciences and Environmental Management, Institute of Land Use, Engineering and Precision Farming Technology, University of Debrecen, 138 Böszörményi St., H-4032 Debrecen, Hungary; (A.S.); (S.M.N.M.); (P.R.); (J.N.); (Á.I.)
| | - Seyed Mohammad Nasir Mousavi
- Faculty of Agricultural and Food Sciences and Environmental Management, Institute of Land Use, Engineering and Precision Farming Technology, University of Debrecen, 138 Böszörményi St., H-4032 Debrecen, Hungary; (A.S.); (S.M.N.M.); (P.R.); (J.N.); (Á.I.)
| | - Csaba Bojtor
- Faculty of Agricultural and Food Sciences and Environmental Management, Institute of Land Use, Engineering and Precision Farming Technology, University of Debrecen, 138 Böszörményi St., H-4032 Debrecen, Hungary; (A.S.); (S.M.N.M.); (P.R.); (J.N.); (Á.I.)
| | - Péter Ragán
- Faculty of Agricultural and Food Sciences and Environmental Management, Institute of Land Use, Engineering and Precision Farming Technology, University of Debrecen, 138 Böszörményi St., H-4032 Debrecen, Hungary; (A.S.); (S.M.N.M.); (P.R.); (J.N.); (Á.I.)
| | - János Nagy
- Faculty of Agricultural and Food Sciences and Environmental Management, Institute of Land Use, Engineering and Precision Farming Technology, University of Debrecen, 138 Böszörményi St., H-4032 Debrecen, Hungary; (A.S.); (S.M.N.M.); (P.R.); (J.N.); (Á.I.)
| | - Attila Vad
- Institutes for Agricultural Research and Educational Farm (IAREF), Farm and Regional Research Institutes of Debrecen (RID), Experimental Station of Látókép, University of Debrecen, H-4032 Debrecen, Hungary;
| | - Árpád Illés
- Faculty of Agricultural and Food Sciences and Environmental Management, Institute of Land Use, Engineering and Precision Farming Technology, University of Debrecen, 138 Böszörményi St., H-4032 Debrecen, Hungary; (A.S.); (S.M.N.M.); (P.R.); (J.N.); (Á.I.)
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22
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Assessing Phytosanitary Application Efficiency of a Boom Sprayer Machine Using RGB Sensor in Grassy Fields. SUSTAINABILITY 2022. [DOI: 10.3390/su14063666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The systematic use of plant protection products is now being called into question with the growing awareness of the risks they can represent for the environment and human health. The application of precision agriculture technologies helps to improve agricultural production but also to rationalize input costs and improve ecological footprints. Here we present a study on fungicide application efficiency and its impact on the grass quality of a golf course green using the free open-source image analysis software FIJI (Image J) to analyze ground RGB (high-resolution digital cameras) and multispectral aerial imagery in combination with experimental data of spray pressure and hydraulic slot nozzle size of a boom sprayer machine. The multivariate regression model best explained variance in the normalized green-red difference index (NGRDI) as a relevant indicator of healthy turfgrass fields from the aerial, ground, and machine data set.
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23
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Sandhu KS, Merrick LF, Sankaran S, Zhang Z, Carter AH. Prospectus of Genomic Selection and Phenomics in Cereal, Legume and Oilseed Breeding Programs. Front Genet 2022. [PMCID: PMC8814369 DOI: 10.3389/fgene.2021.829131] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The last decade witnessed an unprecedented increase in the adoption of genomic selection (GS) and phenomics tools in plant breeding programs, especially in major cereal crops. GS has demonstrated the potential for selecting superior genotypes with high precision and accelerating the breeding cycle. Phenomics is a rapidly advancing domain to alleviate phenotyping bottlenecks and explores new large-scale phenotyping and data acquisition methods. In this review, we discuss the lesson learned from GS and phenomics in six self-pollinated crops, primarily focusing on rice, wheat, soybean, common bean, chickpea, and groundnut, and their implementation schemes are discussed after assessing their impact in the breeding programs. Here, the status of the adoption of genomics and phenomics is provided for those crops, with a complete GS overview. GS’s progress until 2020 is discussed in detail, and relevant information and links to the source codes are provided for implementing this technology into plant breeding programs, with most of the examples from wheat breeding programs. Detailed information about various phenotyping tools is provided to strengthen the field of phenomics for a plant breeder in the coming years. Finally, we highlight the benefits of merging genomic selection, phenomics, and machine and deep learning that have resulted in extraordinary results during recent years in wheat, rice, and soybean. Hence, there is a potential for adopting these technologies into crops like the common bean, chickpea, and groundnut. The adoption of phenomics and GS into different breeding programs will accelerate genetic gain that would create an impact on food security, realizing the need to feed an ever-growing population.
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Affiliation(s)
- Karansher S. Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
- *Correspondence: Karansher S. Sandhu,
| | - Lance F. Merrick
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | - Sindhuja Sankaran
- Department of Biological System Engineering, Washington State University, Pullman, WA, United States
| | - Zhiwu Zhang
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | - Arron H. Carter
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
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24
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Comparison of Multi-Methods for Identifying Maize Phenology Using PhenoCams. REMOTE SENSING 2022. [DOI: 10.3390/rs14020244] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Accurately identifying the phenology of summer maize is crucial for both cultivar breeding and fertilizer controlling in precision agriculture. In this study, daily RGB images covering the entire growth of summer maize were collected using phenocams at sites in Shangqiu (2018, 2019 and 2020) and Nanpi (2020) in China. Four phenological dates, including six leaves, booting, heading and maturity of summer maize, were pre-defined and extracted from the phenocam-based images. The spectral indices, textural indices and integrated spectral and textural indices were calculated using the improved adaptive feature-weighting method. The double logistic function, harmonic analysis of time series, Savitzky–Golay and spline interpolation were applied to filter these indices and pre-defined phenology was identified and compared with the ground observations. The results show that the DLF achieved the highest accuracy, with the coefficient of determination (R2) and the root-mean-square error (RMSE) being 0.86 and 9.32 days, respectively. The new index performed better than the single usage of spectral and textural indices, of which the R2 and RMSE were 0.92 and 9.38 days, respectively. The phenological extraction using the new index and double logistic function based on the PhenoCam data was effective and convenient, obtaining high accuracy. Therefore, it is recommended the adoption of the new index by integrating the spectral and textural indices for extracting maize phenology using PhenoCam data.
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25
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Waiphara P, Bourgenot C, Compton LJ, Prashar A. Optical Imaging Resources for Crop Phenotyping and Stress Detection. Methods Mol Biol 2022; 2494:255-265. [PMID: 35467213 DOI: 10.1007/978-1-0716-2297-1_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
With a rapidly increasing population, diminishing resource availability, and variation in environment, there is a need to change agricultural production to deliver long-term food security. To deliver such change, we need crops that are productive and tolerant to different stress factors. The traditional methods of obtaining data for phenotyping under field conditions, e.g., for morphological traits such as canopy structure or physiological traits such as plant stress-related traits, are laborious and time-consuming. A variety of imaging tools in the visible, spectral, and thermal infrared ranges allow data collection for quantitative studies of complex traits and crop monitoring. These tools can be used on crop phenotyping and monitoring platforms for high-throughput assessment of traits in order to better understand plant stress responses and the physiological pathways underlying yield. The applications and brief review of these imaging techniques are described and discussed in this chapter.
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Affiliation(s)
- Phatchareeya Waiphara
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Cyril Bourgenot
- Precision Optics Laboratory, Durham University, Sedgefield, UK
| | | | - Ankush Prashar
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, UK.
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26
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Morota G, Jarquin D, Campbell MT, Iwata H. Statistical Methods for the Quantitative Genetic Analysis of High-Throughput Phenotyping Data. Methods Mol Biol 2022; 2539:269-296. [PMID: 35895210 DOI: 10.1007/978-1-0716-2537-8_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The advent of plant phenomics, coupled with the wealth of genotypic data generated by next-generation sequencing technologies, provides exciting new resources for investigations into and improvement of complex traits. However, these new technologies also bring new challenges in quantitative genetics, namely, a need for the development of robust frameworks that can accommodate these high-dimensional data. In this chapter, we describe methods for the statistical analysis of high-throughput phenotyping (HTP) data with the goal of enhancing the prediction accuracy of genomic selection (GS). Following the Introduction in Sec. 1, Sec. 2 discusses field-based HTP, including the use of unoccupied aerial vehicles and light detection and ranging, as well as how we can achieve increased genetic gain by utilizing image data derived from HTP. Section 3 considers extending commonly used GS models to integrate HTP data as covariates associated with the principal trait response, such as yield. Particular focus is placed on single-trait, multi-trait, and genotype by environment interaction models. One unique aspect of HTP data is that phenomics platforms often produce large-scale data with high spatial and temporal resolution for capturing dynamic growth, development, and stress responses. Section 4 discusses the utility of a random regression model for performing longitudinal modeling. The chapter concludes with a discussion of some standing issues.
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Affiliation(s)
- Gota Morota
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
| | - Diego Jarquin
- Agronomy Department, University of Florida, Gainesville, FL, USA
| | - Malachy T Campbell
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Hiroyoshi Iwata
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
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27
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Martínez-Peña R, Schlereth A, Höhne M, Encke B, Morcuende R, Nieto-Taladriz MT, Araus JL, Aparicio N, Vicente R. Source-Sink Dynamics in Field-Grown Durum Wheat Under Contrasting Nitrogen Supplies: Key Role of Non-Foliar Organs During Grain Filling. FRONTIERS IN PLANT SCIENCE 2022; 13:869680. [PMID: 35574116 PMCID: PMC9100808 DOI: 10.3389/fpls.2022.869680] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 03/31/2022] [Indexed: 05/08/2023]
Abstract
The integration of high-throughput phenotyping and metabolic approaches is a suitable strategy to study the genotype-by-environment interaction and identify novel traits for crop improvement from canopy to an organ level. Our aims were to study the phenotypic and metabolic traits that are related to grain yield and quality at canopy and organ levels, with a special focus on source-sink coordination under contrasting N supplies. Four modern durum wheat varieties with contrasting grain yield were grown in field conditions under two N fertilization levels in north-eastern Spain. We evaluated canopy vegetation indices taken throughout the growing season, physiological and metabolic traits in different photosynthetic organs (flag leaf blade, sheath, peduncle, awn, glume, and lemma) at anthesis and mid-grain filling stages, and agronomic and grain quality traits at harvest. Low N supply triggered an imbalance of C and N coordination at the whole plant level, leading to a reduction of grain yield and nutrient composition. The activities of key enzymes in C and N metabolism as well as the levels of photoassimilates showed that each organ plays an important role during grain filling, some with a higher photosynthetic capacity, others for nutrient storage for later stages of grain filling, or N assimilation and recycling. Interestingly, the enzyme activities and sucrose content of the ear organs were positively associated with grain yield and quality, suggesting, together with the regression models using isotope signatures, the potential contribution of these organs during grain filling. This study highlights the use of holistic approaches to the identification of novel targets to improve grain yield and quality in C3 cereals and the key role of non-foliar organs at late-growth stages.
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Affiliation(s)
- Raquel Martínez-Peña
- Group of Cereals, Section of Herbaceous, Instituto Tecnológico Agrario de Castilla y León (ITACyL), Junta de Castilla y León, Valladolid, Spain
| | - Armin Schlereth
- Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Melanie Höhne
- Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Beatrice Encke
- Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Rosa Morcuende
- Institute of Natural Resources and Agrobiology of Salamanca (IRNASA), Consejo Superior de Investigaciones Científicas (CSIC), Salamanca, Spain
| | | | - José Luis Araus
- Integrative Crop Ecophysiology Group, Section of Plant Physiology, Faculty of Biology, University of Barcelona, Barcelona, Spain
| | - Nieves Aparicio
- Group of Cereals, Section of Herbaceous, Instituto Tecnológico Agrario de Castilla y León (ITACyL), Junta de Castilla y León, Valladolid, Spain
| | - Rubén Vicente
- Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB NOVA), Plant Ecophysiology and Metabolism Group, Oeiras, Portugal
- *Correspondence: Rubén Vicente
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28
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Xu Y, Shrestha V, Piasecki C, Wolfe B, Hamilton L, Millwood RJ, Mazarei M, Stewart CN. Sustainability Trait Modeling of Field-Grown Switchgrass ( Panicum virgatum) Using UAV-Based Imagery. PLANTS (BASEL, SWITZERLAND) 2021; 10:plants10122726. [PMID: 34961199 PMCID: PMC8709265 DOI: 10.3390/plants10122726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 11/29/2021] [Accepted: 12/07/2021] [Indexed: 05/17/2023]
Abstract
Unmanned aerial vehicles (UAVs) provide an intermediate scale of spatial and spectral data collection that yields increased accuracy and consistency in data collection for morphological and physiological traits than satellites and expanded flexibility and high-throughput compared to ground-based data collection. In this study, we used UAV-based remote sensing for automated phenotyping of field-grown switchgrass (Panicum virgatum), a leading bioenergy feedstock. Using vegetation indices calculated from a UAV-based multispectral camera, statistical models were developed for rust disease caused by Puccinia novopanici, leaf chlorophyll, nitrogen, and lignin contents. For the first time, UAV remote sensing technology was used to explore the potentials for multiple traits associated with sustainable production of switchgrass, and one statistical model was developed for each individual trait based on the statistical correlation between vegetation indices and the corresponding trait. Also, for the first time, lignin content was estimated in switchgrass shoots via UAV-based multispectral image analysis and statistical analysis. The UAV-based models were verified by ground-truthing via correlation analysis between the traits measured manually on the ground-based with UAV-based data. The normalized difference red edge (NDRE) vegetation index outperformed the normalized difference vegetation index (NDVI) for rust disease and nitrogen content, while NDVI performed better than NDRE for chlorophyll and lignin content. Overall, linear models were sufficient for rust disease and chlorophyll analysis, but for nitrogen and lignin contents, nonlinear models achieved better results. As the first comprehensive study to model switchgrass sustainability traits from UAV-based remote sensing, these results suggest that this methodology can be utilized for switchgrass high-throughput phenotyping in the field.
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Affiliation(s)
- Yaping Xu
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA; (Y.X.); (V.S.); (C.P.); (B.W.); (L.H.)
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Vivek Shrestha
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA; (Y.X.); (V.S.); (C.P.); (B.W.); (L.H.)
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Cristiano Piasecki
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA; (Y.X.); (V.S.); (C.P.); (B.W.); (L.H.)
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
- ATSI Brasil Pesquisa e Consultoria, Passo Fundo 99054-328, RS, Brazil
| | - Benjamin Wolfe
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA; (Y.X.); (V.S.); (C.P.); (B.W.); (L.H.)
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Lance Hamilton
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA; (Y.X.); (V.S.); (C.P.); (B.W.); (L.H.)
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Reginald J. Millwood
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA; (Y.X.); (V.S.); (C.P.); (B.W.); (L.H.)
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
- Correspondence: (R.J.M.); (M.M.); (C.N.S.J.)
| | - Mitra Mazarei
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA; (Y.X.); (V.S.); (C.P.); (B.W.); (L.H.)
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
- Correspondence: (R.J.M.); (M.M.); (C.N.S.J.)
| | - Charles Neal Stewart
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA; (Y.X.); (V.S.); (C.P.); (B.W.); (L.H.)
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
- Correspondence: (R.J.M.); (M.M.); (C.N.S.J.)
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29
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Shevyrnogov AP, Emelyanov DV, Malchikov NO, Demyanenko TN, Ivchenko VK, Botvich IY. Early Forecasting of Crop Yields Based on PlanetScope Dove Satellite Data. Biophysics (Nagoya-shi) 2021. [DOI: 10.1134/s0006350921060166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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30
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Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review. REMOTE SENSING 2021. [DOI: 10.3390/rs13193841] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Disease diagnosis is one of the major tasks for increasing food production in agriculture. Although precision agriculture (PA) takes less time and provides a more precise application of agricultural activities, the detection of disease using an Unmanned Aerial System (UAS) is a challenging task. Several Unmanned Aerial Vehicles (UAVs) and sensors have been used for this purpose. The UAVs’ platforms and their peripherals have their own limitations in accurately diagnosing plant diseases. Several types of image processing software are available for vignetting and orthorectification. The training and validation of datasets are important characteristics of data analysis. Currently, different algorithms and architectures of machine learning models are used to classify and detect plant diseases. These models help in image segmentation and feature extractions to interpret results. Researchers also use the values of vegetative indices, such as Normalized Difference Vegetative Index (NDVI), Crop Water Stress Index (CWSI), etc., acquired from different multispectral and hyperspectral sensors to fit into the statistical models to deliver results. There are still various drifts in the automatic detection of plant diseases as imaging sensors are limited by their own spectral bandwidth, resolution, background noise of the image, etc. The future of crop health monitoring using UAVs should include a gimble consisting of multiple sensors, large datasets for training and validation, the development of site-specific irradiance systems, and so on. This review briefly highlights the advantages of automatic detection of plant diseases to the growers.
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31
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Kaur B, Sandhu KS, Kamal R, Kaur K, Singh J, Röder MS, Muqaddasi QH. Omics for the Improvement of Abiotic, Biotic, and Agronomic Traits in Major Cereal Crops: Applications, Challenges, and Prospects. PLANTS 2021; 10:plants10101989. [PMID: 34685799 PMCID: PMC8541486 DOI: 10.3390/plants10101989] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 09/17/2021] [Accepted: 09/18/2021] [Indexed: 12/22/2022]
Abstract
Omics technologies, namely genomics, transcriptomics, proteomics, metabolomics, and phenomics, are becoming an integral part of virtually every commercial cereal crop breeding program, as they provide substantial dividends per unit time in both pre-breeding and breeding phases. Continuous advances in omics assure time efficiency and cost benefits to improve cereal crops. This review provides a comprehensive overview of the established omics methods in five major cereals, namely rice, sorghum, maize, barley, and bread wheat. We cover the evolution of technologies in each omics section independently and concentrate on their use to improve economically important agronomic as well as biotic and abiotic stress-related traits. Advancements in the (1) identification, mapping, and sequencing of molecular/structural variants; (2) high-density transcriptomics data to study gene expression patterns; (3) global and targeted proteome profiling to study protein structure and interaction; (4) metabolomic profiling to quantify organ-level, small-density metabolites, and their composition; and (5) high-resolution, high-throughput, image-based phenomics approaches are surveyed in this review.
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Affiliation(s)
- Balwinder Kaur
- Everglades Research and Education Center, University of Florida, 3200 E. Palm Beach Rd., Belle Glade, FL 33430, USA;
| | - Karansher S. Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99163, USA;
| | - Roop Kamal
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Stadt Seeland, Germany; (R.K.); or (M.S.R.)
| | - Kawalpreet Kaur
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada;
| | - Jagmohan Singh
- Division of Plant Pathology, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India;
| | - Marion S. Röder
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Stadt Seeland, Germany; (R.K.); or (M.S.R.)
| | - Quddoos H. Muqaddasi
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Stadt Seeland, Germany; (R.K.); or (M.S.R.)
- Correspondence: or
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32
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Development and Testing of a UAV-Based Multi-Sensor System for Plant Phenotyping and Precision Agriculture. REMOTE SENSING 2021. [DOI: 10.3390/rs13173517] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Unmanned aerial vehicles have been used widely in plant phenotyping and precision agriculture. Several critical challenges remain, however, such as the lack of cross-platform data acquisition software system, sensor calibration protocols, and data processing methods. This paper developed an unmanned aerial system that integrates three cameras (RGB, multispectral, and thermal) and a LiDAR sensor. Data acquisition software supporting data recording and visualization was implemented to run on the Robot Operating System. The design of the multi-sensor unmanned aerial system was open sourced. A data processing pipeline was proposed to preprocess the raw data and to extract phenotypic traits at the plot level, including morphological traits (canopy height, canopy cover, and canopy volume), canopy vegetation index, and canopy temperature. Protocols for both field and laboratory calibrations were developed for the RGB, multispectral, and thermal cameras. The system was validated using ground data collected in a cotton field. Temperatures derived from thermal images had a mean absolute error of 1.02 °C, and canopy NDVI had a mean relative error of 6.6% compared to ground measurements. The observed error for maximum canopy height was 0.1 m. The results show that the system can be useful for plant breeding and precision crop management.
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33
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Abstract
Remote sensing techniques based on images acquired from unmanned aerial vehicles (UAVs) could represent an effective tool to speed up the data acquisition process in phenotyping trials and, consequently, to reduce the time and cost of the field work. In this study, we assessed the ability of a UAV equipped with RGB-NIR cameras in highlighting differences in geometrical and spectral canopy characteristics between eight olive cultivars planted at different planting distances in a hedgerow olive orchard. The relationships between measured and estimated canopy height, projected canopy area and canopy volume were linear regardless of the different cultivars and planting distances (RMSE of 0.12 m, 0.44 m2 and 0.68 m3, respectively). A good relationship (R2 = 0.95) was found between the pruning mass material weighted on the ground and its volume estimated by aerial images. NDVI measured in February 2019 was related to fruit yield per tree measured in November 2018, whereas no relationships were observed with the fruit yield measured in November 2019 due to abiotic and biotic stresses that occurred before harvest. These results confirm the reliability of UAV imagery and structure from motion techniques in estimating the olive geometrical canopy characteristics and suggest further potential applications of UAVs in early discrimination of yield efficiency between different cultivars and in estimating the pruning material volume.
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Abstract
Predicting soybean [Glycine max (L.) Merr.] seed yield is of interest for crop producers to make important agronomic and economic decisions. Evaluating the soybean canopy across a range of common agronomic practices, using canopy measurements, provides a large inference for soybean producers. The individual and synergistic relationships between fractional green canopy cover (FGCC), photosynthetically active radiation (PAR) interception, and a normalized difference vegetative index (NDVI) measurements taken throughout the growing season to predict soybean seed yield in North Dakota, USA, were investigated in 12 environments. Canopy measurements were evaluated across early and late planting dates, 407,000 and 457,000 seeds ha−1 seeding rates, 0.5 and 0.8 relative maturities, and 30.5 and 61 cm row spacings. The single best yield predictor was an NDVI measurement at R5 (beginning of seed development) with a coefficient of determination of 0.65 followed by an FGCC measurement at R5 (R2 = 0.52). Stepwise and Lasso multiple regression methods were used to select the best prediction models using the canopy measurements explaining 69% and 67% of the variation in yield, respectively. Including plant density, which can be easily measured by a producer, with an individual canopy measurement did not improve the explanation in yield. Using FGCC to estimate yield across the growing season explained a range of 49% to 56% of yield variation, and a single FGCC measurement at R5 (R2 = 0.52) being the most efficient and practical method for a soybean producer to estimate yield.
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Sarkar S, Ramsey AF, Cazenave AB, Balota M. Peanut Leaf Wilting Estimation From RGB Color Indices and Logistic Models. FRONTIERS IN PLANT SCIENCE 2021; 12:658621. [PMID: 34220885 PMCID: PMC8253229 DOI: 10.3389/fpls.2021.658621] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 03/30/2021] [Indexed: 06/13/2023]
Abstract
Peanut (Arachis hypogaea L.) is an important crop for United States agriculture and worldwide. Low soil moisture is a major constraint for production in all peanut growing regions with negative effects on yield quantity and quality. Leaf wilting is a visual symptom of low moisture stress used in breeding to improve stress tolerance, but visual rating is slow when thousands of breeding lines are evaluated and can be subject to personnel scoring bias. Photogrammetry might be used instead. The objective of this article is to determine if color space indices derived from red-green-blue (RGB) images can accurately estimate leaf wilting for breeding selection and irrigation triggering in peanut production. RGB images were collected with a digital camera proximally and aerially by a unmanned aerial vehicle during 2018 and 2019. Visual rating was performed on the same days as image collection. Vegetation indices were intensity, hue, saturation, lightness, a∗, b∗, u∗, v∗, green area (GA), greener area (GGA), and crop senescence index (CSI). In particular, hue, a∗, u∗, GA, GGA, and CSI were significantly (p ≤ 0.0001) associated with leaf wilting. These indices were further used to train an ordinal logistic regression model for wilting estimation. This model had 90% accuracy when images were taken aerially and 99% when images were taken proximally. This article reports on a simple yet key aspect of peanut screening for tolerance to low soil moisture stress and uses novel, fast, cost-effective, and accurate RGB-derived models to estimate leaf wilting.
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Affiliation(s)
- Sayantan Sarkar
- School of Plant and Environmental Sciences, Virginia Tech, Tidewater AREC, Suffolk, VA, United States
| | - A. Ford Ramsey
- Department of Agricultural and Applied Economics, Virginia Tech, Blacksburg, VA, United States
| | - Alexandre-Brice Cazenave
- School of Plant and Environmental Sciences, Virginia Tech, Tidewater AREC, Suffolk, VA, United States
| | - Maria Balota
- School of Plant and Environmental Sciences, Virginia Tech, Tidewater AREC, Suffolk, VA, United States
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Guo W, Carroll ME, Singh A, Swetnam TL, Merchant N, Sarkar S, Singh AK, Ganapathysubramanian B. UAS-Based Plant Phenotyping for Research and Breeding Applications. PLANT PHENOMICS (WASHINGTON, D.C.) 2021; 2021:9840192. [PMID: 34195621 PMCID: PMC8214361 DOI: 10.34133/2021/9840192] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Accepted: 04/29/2021] [Indexed: 05/19/2023]
Abstract
Unmanned aircraft system (UAS) is a particularly powerful tool for plant phenotyping, due to reasonable cost of procurement and deployment, ease and flexibility for control and operation, ability to reconfigure sensor payloads to diversify sensing, and the ability to seamlessly fit into a larger connected phenotyping network. These advantages have expanded the use of UAS-based plant phenotyping approach in research and breeding applications. This paper reviews the state of the art in the deployment, collection, curation, storage, and analysis of data from UAS-based phenotyping platforms. We discuss pressing technical challenges, identify future trends in UAS-based phenotyping that the plant research community should be aware of, and pinpoint key plant science and agronomic questions that can be resolved with the next generation of UAS-based imaging modalities and associated data analysis pipelines. This review provides a broad account of the state of the art in UAS-based phenotyping to reduce the barrier to entry to plant science practitioners interested in deploying this imaging modality for phenotyping in plant breeding and research areas.
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Affiliation(s)
- Wei Guo
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Japan
| | | | - Arti Singh
- Department of Agronomy, Iowa State University, Ames, Iowa, USA
| | | | - Nirav Merchant
- Data Science Institute, University of Arizona, Tucson, USA
| | - Soumik Sarkar
- Department of Mechanical Engineering, Iowa State University, Ames, Iowa, USA
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Prasanna BM, Cairns JE, Zaidi PH, Beyene Y, Makumbi D, Gowda M, Magorokosho C, Zaman-Allah M, Olsen M, Das A, Worku M, Gethi J, Vivek BS, Nair SK, Rashid Z, Vinayan MT, Issa AB, San Vicente F, Dhliwayo T, Zhang X. Beat the stress: breeding for climate resilience in maize for the tropical rainfed environments. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1729-1752. [PMID: 33594449 PMCID: PMC7885763 DOI: 10.1007/s00122-021-03773-7] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Accepted: 01/09/2021] [Indexed: 05/03/2023]
Abstract
Intensive public sector breeding efforts and public-private partnerships have led to the increase in genetic gains, and deployment of elite climate-resilient maize cultivars for the stress-prone environments in the tropics. Maize (Zea mays L.) plays a critical role in ensuring food and nutritional security, and livelihoods of millions of resource-constrained smallholders. However, maize yields in the tropical rainfed environments are now increasingly vulnerable to various climate-induced stresses, especially drought, heat, waterlogging, salinity, cold, diseases, and insect pests, which often come in combinations to severely impact maize crops. The International Maize and Wheat Improvement Center (CIMMYT), in partnership with several public and private sector institutions, has been intensively engaged over the last four decades in breeding elite tropical maize germplasm with tolerance to key abiotic and biotic stresses, using an extensive managed stress screening network and on-farm testing system. This has led to the successful development and deployment of an array of elite stress-tolerant maize cultivars across sub-Saharan Africa, Asia, and Latin America. Further increasing genetic gains in the tropical maize breeding programs demands judicious integration of doubled haploidy, high-throughput and precise phenotyping, genomics-assisted breeding, breeding data management, and more effective decision support tools. Multi-institutional efforts, especially public-private alliances, are key to ensure that the improved maize varieties effectively reach the climate-vulnerable farming communities in the tropics, including accelerated replacement of old/obsolete varieties.
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Affiliation(s)
- Boddupalli M Prasanna
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, P.O.Box 1041-00621, Nairobi, Kenya.
| | | | - P H Zaidi
- CIMMYT, ICRISAT Campus, Patancheru, Greater Hyderabad, Telangana, India
| | - Yoseph Beyene
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, P.O.Box 1041-00621, Nairobi, Kenya
| | - Dan Makumbi
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, P.O.Box 1041-00621, Nairobi, Kenya
| | - Manje Gowda
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, P.O.Box 1041-00621, Nairobi, Kenya
| | | | | | - Mike Olsen
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, P.O.Box 1041-00621, Nairobi, Kenya
| | - Aparna Das
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, P.O.Box 1041-00621, Nairobi, Kenya
| | - Mosisa Worku
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, P.O.Box 1041-00621, Nairobi, Kenya
| | | | - B S Vivek
- CIMMYT, ICRISAT Campus, Patancheru, Greater Hyderabad, Telangana, India
| | - Sudha K Nair
- CIMMYT, ICRISAT Campus, Patancheru, Greater Hyderabad, Telangana, India
| | - Zerka Rashid
- CIMMYT, ICRISAT Campus, Patancheru, Greater Hyderabad, Telangana, India
| | - M T Vinayan
- CIMMYT, ICRISAT Campus, Patancheru, Greater Hyderabad, Telangana, India
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Temporal Vegetation Indices and Plant Height from Remotely Sensed Imagery Can Predict Grain Yield and Flowering Time Breeding Value in Maize via Machine Learning Regression. REMOTE SENSING 2021. [DOI: 10.3390/rs13112141] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Unoccupied aerial system (UAS; i.e., drone equipped with sensors) field-based high-throughput phenotyping (HTP) platforms are used to collect high quality images of plant nurseries to screen genetic materials (e.g., hybrids and inbreds) throughout plant growth at relatively low cost. In this study, a set of 100 advanced breeding maize (Zea mays L.) hybrids were planted at optimal (OHOT trial) and delayed planting dates (DHOT trial). Twelve UAS surveys were conducted over the trials throughout the growing season. Fifteen vegetative indices (VIs) and the 99th percentile canopy height measurement (CHMs) were extracted from processed UAS imagery (orthomosaics and point clouds) which were used to predict plot-level grain yield, days to anthesis (DTA), and silking (DTS). A novel statistical approach utilizing a nested design was fit to predict temporal best linear unbiased predictors (TBLUP) for the combined temporal UAS data. Our results demonstrated machine learning-based regressions (ridge, lasso, and elastic net) had from 4- to 9-fold increases in the prediction accuracies and from 13- to 73-fold reductions in root mean squared error (RMSE) compared to classical linear regression in prediction of grain yield or flowering time. Ridge regression performed best in predicting grain yield (prediction accuracy = ~0.6), while lasso and elastic net regressions performed best in predicting DTA and DTS (prediction accuracy = ~0.8) consistently in both trials. We demonstrated that predictor variable importance descended towards the terminal stages of growth, signifying the importance of phenotype collection beyond classical terminal growth stages. This study is among the first to demonstrate an ability to predict yield in elite hybrid maize breeding trials using temporal UAS image-based phenotypes and supports the potential benefit of phenomic selection approaches in estimating breeding values before harvest.
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Yao L, van de Zedde R, Kowalchuk G. Recent developments and potential of robotics in plant eco-phenotyping. Emerg Top Life Sci 2021; 5:289-300. [PMID: 34013965 PMCID: PMC8166337 DOI: 10.1042/etls20200275] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 04/12/2021] [Accepted: 04/13/2021] [Indexed: 02/04/2023]
Abstract
Automated acquisition of plant eco-phenotypic information can serve as a decision-making basis for precision agricultural management and can also provide detailed insights into plant growth status, pest management, water and fertilizer management for plant breeders and plant physiologists. Because the microscopic components and macroscopic morphology of plants will be affected by the ecological environment, research on plant eco-phenotyping is more meaningful than the study of single-plant phenotyping. To achieve high-throughput acquisition of phenotyping information, the combination of high-precision sensors and intelligent robotic platforms have become an emerging research focus. Robotic platforms and automated systems are the important carriers of phenotyping monitoring sensors that enable large-scale screening. Through the diverse design and flexible systems, an efficient operation can be achieved across a range of experimental and field platforms. The combination of robot technology and plant phenotyping monitoring tools provides the data to inform novel artificial intelligence (AI) approaches that will provide steppingstones for new research breakthroughs. Therefore, this article introduces robotics and eco-phenotyping and examines research significant to this novel domain of plant eco-phenotyping. Given the monitoring scenarios of phenotyping information at different scales, the used intelligent robot technology, efficient automation platform, and advanced sensor equipment are summarized in detail. We further discuss the challenges posed to current research as well as the future developmental trends in the application of robot technology and plant eco-phenotyping. These include the use of collected data for AI applications and high-bandwidth data transfer, and large well-structured (meta) data storage approaches in plant sciences and agriculture.
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Affiliation(s)
- Lili Yao
- Wageningen University & Research, Wageningen, Netherlands
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Zhou S, Chai X, Yang Z, Wang H, Yang C, Sun T. Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping. PLANT METHODS 2021; 17:48. [PMID: 33926480 PMCID: PMC8086349 DOI: 10.1186/s13007-021-00747-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 04/19/2021] [Indexed: 05/26/2023]
Abstract
BACKGROUND Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. RESULTS On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. CONCLUSION The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.
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Affiliation(s)
- Shuo Zhou
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St., Beijing, 100081 China
- Key Laboratory of Big Agri-Data, Ministry of Agriculture, Beijing, China
| | - Xiujuan Chai
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St., Beijing, 100081 China
- Key Laboratory of Big Agri-Data, Ministry of Agriculture, Beijing, China
| | - Zixuan Yang
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St., Beijing, 100081 China
| | - Hongwu Wang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St., Beijing, 100081 China
| | - Chenxue Yang
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St., Beijing, 100081 China
| | - Tan Sun
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St., Beijing, 100081 China
- Key Laboratory of Big Agri-Data, Ministry of Agriculture, Beijing, China
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Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases. REMOTE SENSING 2021. [DOI: 10.3390/rs13071238] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The development of UAV (unmanned aerial vehicle) imaging technologies for precision farming applications is rapid, and new studies are published frequently. In cases where measurements are based on aerial imaging, there is the need to have ground truth or reference data in order to develop reliable applications. However, in several precision farming use cases such as pests, weeds, and diseases detection, the reference data can be subjective or relatively difficult to capture. Furthermore, the collection of reference data is usually laborious and time consuming. It also appears that it is difficult to develop generalisable solutions for these areas. This review studies previous research related to pests, weeds, and diseases detection and mapping using UAV imaging in the precision farming context, underpinning the applied reference measurement techniques. The majority of the reviewed studies utilised subjective visual observations of UAV images, and only a few applied in situ measurements. The conclusion of the review is that there is a lack of quantitative and repeatable reference data measurement solutions in the areas of mapping pests, weeds, and diseases. In addition, the results that the studies present should be reflected in the applied references. An option in the future approach could be the use of synthetic data as reference.
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A Technical Study on UAV Characteristics for Precision Agriculture Applications and Associated Practical Challenges. REMOTE SENSING 2021. [DOI: 10.3390/rs13061204] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The incorporation of advanced technologies into Unmanned Aerial Vehicles (UAVs) platforms have enabled many practical applications in Precision Agriculture (PA) over the past decade. These PA tools offer capabilities that increase agricultural productivity and inputs’ efficiency and minimize operational costs simultaneously. However, these platforms also have some constraints that limit the application of UAVs in agricultural operations. The constraints include limitations in providing imagery of adequate spatial and temporal resolutions, dependency on weather conditions, and geometric and radiometric correction requirements. In this paper, a practical guide on technical characterizations of common types of UAVs used in PA is presented. This paper helps select the most suitable UAVs and on-board sensors for different agricultural operations by considering all the possible constraints. Over a hundred research studies were reviewed on UAVs applications in PA and practical challenges in monitoring and mapping field crops. We concluded by providing suggestions and future directions to overcome challenges in optimizing operational proficiency.
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Assimilation of LAI Derived from UAV Multispectral Data into the SAFY Model to Estimate Maize Yield. REMOTE SENSING 2021. [DOI: 10.3390/rs13061094] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In this study, we develop a method to estimate corn yield based on remote sensing data and ground monitoring data under different water treatments. Spatially explicit information on crop yields is essential for farmers and agricultural agencies to make well-informed decisions. One approach to estimate crop yield with remote sensing is data assimilation, which integrates sequential observations of canopy development from remote sensing into model simulations of crop growth processes. We found that leaf area index (LAI) inversion based on unmanned aerial vehicle (UAV) vegetation index has a high accuracy, with R2 and root mean square error (RMSE) values of 0.877 and 0.609, respectively. Maize yield estimation based on UAV remote sensing data and simple algorithm for yield (SAFY) crop model data assimilation has different yield estimation accuracy under different water treatments. This method can be used to estimate corn yield, where R2 is 0.855 and RMSE is 692.8kg/ha. Generally, the higher the water stress, the lower the estimation accuracy. Furthermore, we perform the yield estimate mapping at 2 m spatial resolution, which has a higher spatial resolution and accuracy than satellite remote sensing. The great potential of incorporating UAV observations with crop data to monitor crop yield, and improve agricultural management is therefore indicated.
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Che S, Du G, Wang N, He K, Mo Z, Sun B, Chen Y, Cao Y, Wang J, Mao Y. Biomass estimation of cultivated red algae Pyropia using unmanned aerial platform based multispectral imaging. PLANT METHODS 2021; 17:12. [PMID: 33541365 PMCID: PMC7863433 DOI: 10.1186/s13007-021-00711-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 01/18/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Pyropia is an economically advantageous genus of red macroalgae, which has been cultivated in the coastal areas of East Asia for over 300 years. Realizing estimation of macroalgae biomass in a high-throughput way would great benefit their cultivation management and research on breeding and phenomics. However, the conventional method is labour-intensive, time-consuming, manually destructive, and prone to human error. Nowadays, high-throughput phenotyping using unmanned aerial vehicle (UAV)-based spectral imaging is widely used for terrestrial crops, grassland, and forest, but no such application in marine aquaculture has been reported. RESULTS In this study, multispectral images of cultivated Pyropia yezoensis were taken using a UAV system in the north of Haizhou Bay in the midwestern coast of Yellow Sea. The exposure period of P. yezoensis was utilized to prevent the significant shielding effect of seawater on the reflectance spectrum. The vegetation indices of normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI) and normalized difference of red edge (NDRE) were derived and indicated no significant difference between the time that P. yezoensis was completely exposed to the air and 1 h later. The regression models of the vegetation indices and P. yezoensis biomass per unit area were established and validated. The quadratic model of DVI (Biomass = - 5.550DVI2 + 105.410DVI + 7.530) showed more accuracy than the other index or indices combination, with the highest coefficient of determination (R2), root mean square error (RMSE), and relative estimated accuracy (Ac) values of 0.925, 8.06, and 74.93%, respectively. The regression model was further validated by consistently predicting the biomass with a high R2 value of 0.918, RMSE of 8.80, and Ac of 82.25%. CONCLUSIONS This study suggests that the biomass of Pyropia can be effectively estimated using UAV-based spectral imaging with high accuracy and consistency. It also implied that multispectral aerial imaging is potential to assist digital management and phenomics research on cultivated macroalgae in a high-throughput way.
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Affiliation(s)
- Shuai Che
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003 People’s Republic of China
| | - Guoying Du
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003 People’s Republic of China
| | - Ning Wang
- Xi’ an Ecotech Spectral Imaging and Eco-drone Remote Sensing Research Center Co., Ltd., Xi’ an, 710000 People’s Republic of China
| | - Kun He
- Xi’ an Ecotech Spectral Imaging and Eco-drone Remote Sensing Research Center Co., Ltd., Xi’ an, 710000 People’s Republic of China
| | - Zhaolan Mo
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003 People’s Republic of China
| | - Bin Sun
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003 People’s Republic of China
| | - Yu Chen
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003 People’s Republic of China
| | - Yifei Cao
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003 People’s Republic of China
| | - Junhao Wang
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003 People’s Republic of China
| | - Yunxiang Mao
- Key Laboratory of Utilization and Conservation of Tropical Marine Bioresource (Ministry of Education), College of Fisheries and Life Science, Hainan Tropical Ocean University, Sanya, 572022 People’s Republic of China
- Laboratory for Marine Biology and Biotechnology, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao, 266000 People’s Republic of China
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Galli G, Sabadin F, Costa-Neto GMF, Fritsche-Neto R. A novel way to validate UAS-based high-throughput phenotyping protocols using in silico experiments for plant breeding purposes. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:715-730. [PMID: 33216217 DOI: 10.1007/s00122-020-03726-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 11/06/2020] [Indexed: 06/11/2023]
Abstract
It is possible to make inferences regarding the feasibility and applicability of plant high-throughput phenotyping via computer simulations. Protocol validation has been a key challenge to the establishment of high-throughput phenotyping (HTP) in breeding programs. We add to this matter by proposing an innovative way for designing and validating aerial imagery-based HTP approaches with in silico 3D experiments for plant breeding purposes. The algorithm is constructed following a pipeline composed of the simulation of phenotypic values, three-dimensional modeling of trials, and image rendering. Our tool is exemplified by testing a set of experimental setups that are of interest in the context of maize breeding using a comprehensive case study. We report on how the choice of (percentile of) points in dense clouds, the experimental repeatability (heritability), the treatment variance (genetic variability), and the flight altitude affect the accuracy of high-throughput plant height estimation based on conventional structure-from-motion (SfM) and multi-view stereo (MVS) pipelines. The evaluation of both the algorithm and the case study was driven by comparisons of the computer-simulated (ground truth) and the HTP-estimated values using correlations, regressions, and similarity indices. Our results showed that the 3D experiments can be adequately reconstructed, enabling inference-making. Moreover, it suggests that treatment variance, repeatability, and the choice of the percentile of points are highly influential over the accuracy of HTP. Conversely, flight altitude influenced the quality of reconstruction but not the accuracy of plant height estimation. Therefore, we believe that our tool can be of high value, enabling the promotion of new insights and further understanding of the events underlying the practice of high-throughput phenotyping.
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Affiliation(s)
- Giovanni Galli
- Department of Genetics, "Luiz de Queiroz" College of Agriculture, University of São Paulo, Piracicaba, São Paulo, Brazil.
| | - Felipe Sabadin
- Department of Genetics, "Luiz de Queiroz" College of Agriculture, University of São Paulo, Piracicaba, São Paulo, Brazil
| | | | - Roberto Fritsche-Neto
- Department of Genetics, "Luiz de Queiroz" College of Agriculture, University of São Paulo, Piracicaba, São Paulo, Brazil
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Li D, Quan C, Song Z, Li X, Yu G, Li C, Muhammad A. High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field. Front Bioeng Biotechnol 2021; 8:623705. [PMID: 33520974 PMCID: PMC7838587 DOI: 10.3389/fbioe.2020.623705] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 12/15/2020] [Indexed: 11/13/2022] Open
Abstract
Food scarcity, population growth, and global climate change have propelled crop yield growth driven by high-throughput phenotyping into the era of big data. However, access to large-scale phenotypic data has now become a critical barrier that phenomics urgently must overcome. Fortunately, the high-throughput plant phenotyping platform (HT3P), employing advanced sensors and data collection systems, can take full advantage of non-destructive and high-throughput methods to monitor, quantify, and evaluate specific phenotypes for large-scale agricultural experiments, and it can effectively perform phenotypic tasks that traditional phenotyping could not do. In this way, HT3Ps are novel and powerful tools, for which various commercial, customized, and even self-developed ones have been recently introduced in rising numbers. Here, we review these HT3Ps in nearly 7 years from greenhouses and growth chambers to the field, and from ground-based proximal phenotyping to aerial large-scale remote sensing. Platform configurations, novelties, operating modes, current developments, as well the strengths and weaknesses of diverse types of HT3Ps are thoroughly and clearly described. Then, miscellaneous combinations of HT3Ps for comparative validation and comprehensive analysis are systematically present, for the first time. Finally, we consider current phenotypic challenges and provide fresh perspectives on future development trends of HT3Ps. This review aims to provide ideas, thoughts, and insights for the optimal selection, exploitation, and utilization of HT3Ps, and thereby pave the way to break through current phenotyping bottlenecks in botany.
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Affiliation(s)
- Daoliang Li
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Chaoqun Quan
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Zhaoyang Song
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Xiang Li
- Department of Psychology, College of Education, Hubei University, Wuhan, China
| | - Guanghui Yu
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Cheng Li
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Akhter Muhammad
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
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Kar S, Garin V, Kholová J, Vadez V, Durbha SS, Tanaka R, Iwata H, Urban MO, Adinarayana J. SpaTemHTP: A Data Analysis Pipeline for Efficient Processing and Utilization of Temporal High-Throughput Phenotyping Data. FRONTIERS IN PLANT SCIENCE 2020; 11:552509. [PMID: 33329623 PMCID: PMC7714717 DOI: 10.3389/fpls.2020.552509] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 10/19/2020] [Indexed: 06/12/2023]
Abstract
The rapid development of phenotyping technologies over the last years gave the opportunity to study plant development over time. The treatment of the massive amount of data collected by high-throughput phenotyping (HTP) platforms is however an important challenge for the plant science community. An important issue is to accurately estimate, over time, the genotypic component of plant phenotype. In outdoor and field-based HTP platforms, phenotype measurements can be substantially affected by data-generation inaccuracies or failures, leading to erroneous or missing data. To solve that problem, we developed an analytical pipeline composed of three modules: detection of outliers, imputation of missing values, and mixed-model genotype adjusted means computation with spatial adjustment. The pipeline was tested on three different traits (3D leaf area, projected leaf area, and plant height), in two crops (chickpea, sorghum), measured during two seasons. Using real-data analyses and simulations, we showed that the sequential application of the three pipeline steps was particularly useful to estimate smooth genotype growth curves from raw data containing a large amount of noise, a situation that is potentially frequent in data generated on outdoor HTP platforms. The procedure we propose can handle up to 50% of missing values. It is also robust to data contamination rates between 20 and 30% of the data. The pipeline was further extended to model the genotype time series data. A change-point analysis allowed the determination of growth phases and the optimal timing where genotypic differences were the largest. The estimated genotypic values were used to cluster the genotypes during the optimal growth phase. Through a two-way analysis of variance (ANOVA), clusters were found to be consistently defined throughout the growth duration. Therefore, we could show, on a wide range of scenarios, that the pipeline facilitated efficient extraction of useful information from outdoor HTP platform data. High-quality plant growth time series data is also provided to support breeding decisions. The R code of the pipeline is available at https://github.com/ICRISAT-GEMS/SpaTemHTP.
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Affiliation(s)
- Soumyashree Kar
- Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Vincent Garin
- Crop Physiology, International Crop Research Institute for Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Jana Kholová
- Crop Physiology, International Crop Research Institute for Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Vincent Vadez
- Crop Physiology, International Crop Research Institute for Semi-Arid Tropics (ICRISAT), Hyderabad, India
- Institut de Recherche pour le Développement (IRD) – Université de Montpellier – UMR DIADE, Montpellier, France
| | - Surya S. Durbha
- Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Ryokei Tanaka
- Laboratory of Biometrics and Bioinformatics, University of Tokyo, Tokyo, Japan
| | - Hiroyoshi Iwata
- Laboratory of Biometrics and Bioinformatics, University of Tokyo, Tokyo, Japan
| | - Milan O. Urban
- Bean Physiology - Agrobiodiversity, Alliance of Bioversity International and CIAT, Cali, Colombia
| | - J. Adinarayana
- Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, India
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48
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Detection of Invasive Species in Wetlands: Practical DL with Heavily Imbalanced Data. REMOTE SENSING 2020. [DOI: 10.3390/rs12203431] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Deep Learning (DL) has become popular due to its ease of use and accuracy, with Transfer Learning (TL) effectively reducing the number of images needed to solve environmental problems. However, this approach has some limitations which we set out to explore: Our goal is to detect the presence of an invasive blueberry species in aerial images of wetlands. This is a key problem in ecosystem protection which is also challenging in terms of DL due to the severe imbalance present in the data. Results for the ResNet50 network show a high classification accuracy while largely ignoring the blueberry class, rendering these results of limited practical interest to detect that specific class. Moreover, by using loss function weighting and data augmentation results more akin to our practical application, our goals can be obtained. Our experiments regarding TL show that ImageNet weights do not produce satisfactory results when only the final layer of the network is trained. Furthermore, only minor gains are obtained compared with random weights when the whole network is retrained. Finally, in a study of state-of-the-art DL architectures best results were obtained by the ResNeXt architecture with 93.75 True Positive Rate and 98.11 accuracy for the Blueberry class with ResNet50, Densenet, and wideResNet obtaining close results.
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49
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Leveraging Very-High Spatial Resolution Hyperspectral and Thermal UAV Imageries for Characterizing Diurnal Indicators of Grapevine Physiology. REMOTE SENSING 2020. [DOI: 10.3390/rs12193216] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Efficient and accurate methods to monitor crop physiological responses help growers better understand crop physiology and improve crop productivity. In recent years, developments in unmanned aerial vehicles (UAV) and sensor technology have enabled image acquisition at very-high spectral, spatial, and temporal resolutions. However, potential applications and limitations of very-high-resolution (VHR) hyperspectral and thermal UAV imaging for characterization of plant diurnal physiology remain largely unknown, due to issues related to shadow and canopy heterogeneity. In this study, we propose a canopy zone-weighting (CZW) method to leverage the potential of VHR (≤9 cm) hyperspectral and thermal UAV imageries in estimating physiological indicators, such as stomatal conductance (Gs) and steady-state fluorescence (Fs). Diurnal flights and concurrent in-situ measurements were conducted during grapevine growing seasons in 2017 and 2018 in a vineyard in Missouri, USA. We used neural net classifier and the Canny edge detection method to extract pure vine canopy from the hyperspectral and thermal images, respectively. Then, the vine canopy was segmented into three canopy zones (sunlit, nadir, and shaded) using K-means clustering based on the canopy shadow fraction and canopy temperature. Common reflectance-based spectral indices, sun-induced chlorophyll fluorescence (SIF), and simplified canopy water stress index (siCWSI) were computed as image retrievals. Using the coefficient of determination (R2) established between the image retrievals from three canopy zones and the in-situ measurements as a weight factor, weighted image retrievals were calculated and their correlation with in-situ measurements was explored. The results showed that the most frequent and the highest correlations were found for Gs and Fs, with CZW-based Photochemical reflectance index (PRI), SIF, and siCWSI (PRICZW, SIFCZW, and siCWSICZW), respectively. When all flights combined for the given field campaign date, PRICZW, SIFCZW, and siCWSICZW significantly improved the relationship with Gs and Fs. The proposed approach takes full advantage of VHR hyperspectral and thermal UAV imageries, and suggests that the CZW method is simple yet effective in estimating Gs and Fs.
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Leaf versus whole-canopy remote sensing methodologies for crop monitoring under conservation agriculture: a case of study with maize in Zimbabwe. Sci Rep 2020; 10:16008. [PMID: 32994539 PMCID: PMC7524805 DOI: 10.1038/s41598-020-73110-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 09/10/2020] [Indexed: 11/29/2022] Open
Abstract
Enhancing nitrogen fertilization efficiency for improving yield is a major challenge for smallholder farming systems. Rapid and cost-effective methodologies with the capability to assess the effects of fertilization are required to facilitate smallholder farm management. This study compares maize leaf and canopy-based approaches for assessing N fertilization performance under different tillage, residue coverage and top-dressing conditions in Zimbabwe. Among the measurements made on individual leaves, chlorophyll readings were the best indicators for both N content in leaves (R < 0.700) and grain yield (GY) (R < 0.800). Canopy indices reported even higher correlation coefficients when assessing GY, especially those based on the measurements of the vegetation density as the green area indices (R < 0.850). Canopy measurements from both ground and aerial platforms performed very similar, but indices assessed from the UAV performed best in capturing the most relevant information from the whole plot and correlations with GY and leaf N content were slightly higher. Leaf-based measurements demonstrated utility in monitoring N leaf content, though canopy measurements outperformed the leaf readings in assessing GY parameters, while providing the additional value derived from the affordability and easiness of using a pheno-pole system or the high-throughput capacities of the UAVs.
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