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Kwon SH, Ku KB, Le AT, Han GD, Park Y, Kim J, Tuan TT, Chung YS, Mansoor S. Enhancing citrus fruit yield investigations through flight height optimization with UAV imaging. Sci Rep 2024; 14:322. [PMID: 38172521 PMCID: PMC10764763 DOI: 10.1038/s41598-023-50921-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 12/28/2023] [Indexed: 01/05/2024] Open
Abstract
Citrus fruit yield is essential for market stability, as it allows businesses to plan for production and distribution. However, yield estimation is a complex and time-consuming process that often requires a large number of field samples to ensure representativeness. To address this challenge, we investigated the optimal altitude for unmanned aerial vehicle (UAV) imaging to estimate the yield of Citrus unshiu fruit. We captured images from five different altitudes (30 m, 50 m, 70 m, 90 m, and 110 m), and determined that a resolution of approximately 5 pixels/cm is necessary for reliable estimation of fruit size based on the average diameter of C. unshiu fruit (46.7 mm). Additionally, we found that histogram equalization of the images improved fruit count estimation compared to using untreated images. At the images from 30 m height, the normal image estimates fruit numbers as 73, 55, and 88. However, the histogram equalized image estimates 88, 71, 105. The actual number of fruits is 124, 88, and 141. Using a Vegetation Index such as IPCA showed a similar estimation value to histogram equalization, but I1 estimation represents a gap to actual yields. Our results provide a valuable database for future UAV field investigations of citrus fruit yield. Using flying platforms like UAVs can provide a step towards adopting this sort of model spanning ever greater regions at a cheap cost, with this system generating accurate results in this manner.
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Affiliation(s)
- Soon-Hwa Kwon
- Citrus Research Institute, National Institute of Horticultural and Herbal Science, Rural Development Administration, Jeju, 63607, Republic of Korea
| | - Ki Bon Ku
- Department of Plant Resources and Environment, Jeju National University, Jeju, 63243, Republic of Korea
| | - Anh Tuan Le
- Department of Plant Resources and Environment, Jeju National University, Jeju, 63243, Republic of Korea
| | - Gyung Deok Han
- Department of Practical Arts Education, Cheongju National University of Education, Cheongju, 28690, Republic of Korea
| | - Yosup Park
- Citrus Research Institute, National Institute of Horticultural and Herbal Science, Rural Development Administration, Jeju, 63607, Republic of Korea
| | - Jaehong Kim
- Citrus Research Institute, National Institute of Horticultural and Herbal Science, Rural Development Administration, Jeju, 63607, Republic of Korea
| | - Thai Thanh Tuan
- Department of Plant Resources and Environment, Jeju National University, Jeju, 63243, Republic of Korea
| | - Yong Suk Chung
- Department of Plant Resources and Environment, Jeju National University, Jeju, 63243, Republic of Korea.
| | - Sheikh Mansoor
- Department of Plant Resources and Environment, Jeju National University, Jeju, 63243, Republic of Korea.
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Buunk T, Vélez S, Ariza-Sentís M, Valente J. Comparing Nadir and Oblique Thermal Imagery in UAV-Based 3D Crop Water Stress Index Applications for Precision Viticulture with LiDAR Validation. SENSORS (BASEL, SWITZERLAND) 2023; 23:8625. [PMID: 37896718 PMCID: PMC10610640 DOI: 10.3390/s23208625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/06/2023] [Accepted: 10/19/2023] [Indexed: 10/29/2023]
Abstract
Unmanned Aerial Vehicle (UAV) thermal imagery is rapidly becoming an essential tool in precision agriculture. Its ability to enable widespread crop status assessment is increasingly critical, given escalating water demands and limited resources, which drive the need for optimizing water use and crop yield through well-planned irrigation and vegetation management. Despite advancements in crop assessment methodologies, including the use of vegetation indices, 2D mapping, and 3D point cloud technologies, some aspects remain less understood. For instance, mission plans often capture nadir and oblique images simultaneously, which can be time- and resource-intensive, without a clear understanding of each image type's impact. This issue is particularly critical for crops with specific growth patterns, such as woody crops, which grow vertically. This research aims to investigate the role of nadir and oblique images in the generation of CWSI (Crop Water Stress Index) maps and CWSI point clouds, that is 2D and 3D products, in woody crops for precision agriculture. To this end, products were generated using Agisoft Metashape, ArcGIS Pro, and CloudCompare to explore the effects of various flight configurations on the final outcome, seeking to identify the most efficient workflow for each remote sensing product. A linear regression analysis reveals that, for generating 2D products (orthomosaics), combining flight angles is redundant, while 3D products (point clouds) are generated equally from nadir and oblique images. Volume calculations show that combining nadir and oblique flights yields the most accurate results for CWSI point clouds compared to LiDAR in terms of geometric representation (R2 = 0.72), followed by the nadir flight (R2 = 0.68), and, finally, the oblique flight (R2 = 0.54). Thus, point clouds offer a fuller perspective of the canopy. To our knowledge, this is the first time that CWSI point clouds have been used for precision viticulture, and this knowledge can aid farm managers, technicians, or UAV pilots in optimizing the capture of UAV image datasets in line with their specific goals.
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Affiliation(s)
- Thomas Buunk
- Laboratory of Geo-Information Sciences and Remote Sensing, Wageningen University & Research, 6708 PB Wageningen, The Netherlands;
| | - Sergio Vélez
- Information Technology Group, Wageningen University & Research, 6708 PB Wageningen, The Netherlands; (M.A.-S.); (J.V.)
| | - Mar Ariza-Sentís
- Information Technology Group, Wageningen University & Research, 6708 PB Wageningen, The Netherlands; (M.A.-S.); (J.V.)
| | - João Valente
- Information Technology Group, Wageningen University & Research, 6708 PB Wageningen, The Netherlands; (M.A.-S.); (J.V.)
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Singh AP, Yerudkar A, Liuzza D, Liu Y, Glielmo L. An Optimal Decision Support System Based on Crop Dynamic Model for N-Fertilizer Treatment. SENSORS (BASEL, SWITZERLAND) 2022; 22:7613. [PMID: 36236710 PMCID: PMC9570642 DOI: 10.3390/s22197613] [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: 08/20/2022] [Revised: 10/04/2022] [Accepted: 10/05/2022] [Indexed: 06/16/2023]
Abstract
The efficient handling of nitrogen has become a critical issue in modern agriculture, from a financial standpoint, as well as in regard to reducing the environmental impacts of using an excessive amount of nitrogen fertilizer. Manure compost is useful for maintaining or raising soil chemical levels without excessive NO3- accumulation; however, for the best grain yield, it should be combined with N fertilizer. Via this study, we aimed to develop an optimal decision support system that indicates when to initiate fertilization based on nitrogen-limited (N-limited) crop growth dynamics. An optimal nitrogen fertilizer (N-fertilizer) management system increases crop yield while maintaining a balance between fertilizer supply and crop demand. This study used the N-limited crop growth model (LINTUL3) to develop an optimal decision support system. In this work, we formulated and resolved two optimization challenges: (i) maximization of biomass growth; and (ii) maximization of growth with the least cost paid on N-fertilizer and its application. Furthermore, two case studies were developed based on the number of fields: (i) optimization for a single field, and (ii) optimization for multiple fields. In the case of multiple fields, it is hypothesized that a fertilizer treatment for one field can leak to other fields and affect the nitrogen dynamics of different fields. Finally, numerical simulations were carried out supporting the theory developed in the paper. The simulations showed that when the proposed work was employed to achieve the goal of optimal nitrogen management for a crop, a 28% to 53% increase in biomass growth under certain scenarios was attained.
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Affiliation(s)
- Abhaya Pal Singh
- Department of Engineering, University of Sannio, 82100 Benevento, Italy
| | - Amol Yerudkar
- Department of Engineering, University of Sannio, 82100 Benevento, Italy
| | - Davide Liuzza
- ENEA Fusion and Nuclear Safety Department, 00044 Frascati, Italy
| | - Yang Liu
- College of Mathematics and Computer Science, and College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, China
- Jinhua Intelligent Manufacturing Research Institute, Jinhua 321032, China
| | - Luigi Glielmo
- Department of Engineering, University of Sannio, 82100 Benevento, Italy
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Satellite Altimetry: Achievements and Future Trends by a Scientometrics Analysis. REMOTE SENSING 2022. [DOI: 10.3390/rs14143332] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Scientometric reviews, facilitated by computational and visual analytical approaches, allow researchers to gain a thorough understanding of research trends and areas of concentration from a large number of publications. With the fast development of satellite altimetry, which has been effectively applied to a wide range of research topics, it is timely to summarize the scientific achievements of the previous 50 years and identify future trends in this field. A comprehensive overview of satellite altimetry was presented using a total of 8541 publications from the Web of Science Core Collection covering the years from 1970 to 2021. We begin by presenting the fundamental statistical results of the publications, such as the annual number of papers, study categories, countries/regions, afflictions, journals, authors, and keywords, in order to provide a comprehensive picture of satellite altimetry research. We discuss the co-occurrence of the authors in order to reveal the global collaboration network of satellite altimetry research. Finally, we utilised co-citation networks to detect the development trend and associated crucial publications for various specific topics. The findings show that satellite altimetry research has been changed immensely during the last half-century. The United States, France, China, England, and Germany made the most significant contributions in the field of satellite altimetry. The analysis reveals a clear link between technology advancements and the trend in satellite altimetry research. As a result, wide swath altimetry, GNSS-reflectometry, laser altimetry, terrestrial hydrology, and deep learning are among the most frontier study subjects. The findings of this work could guide a thorough understanding of satellite altimetry’s overall development and research front.
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Matese A, Di Gennaro SF, Orlandi G, Gatti M, Poni S. Assessing Grapevine Biophysical Parameters From Unmanned Aerial Vehicles Hyperspectral Imagery. FRONTIERS IN PLANT SCIENCE 2022; 13:898722. [PMID: 35769294 PMCID: PMC9235871 DOI: 10.3389/fpls.2022.898722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 05/12/2022] [Indexed: 05/04/2023]
Abstract
Over the last 50 years, many approaches for extracting plant key parameters from remotely sensed data have been developed, especially in the last decade with the spread of unmanned aerial vehicles (UAVs) in agriculture. Multispectral sensors are very useful for the elaboration of common vegetation indices (VIs), however, the spectral accuracy and range may not be enough. In this scenario, hyperspectral (HS) technologies are gaining particular attention thanks to the highest spectral resolution, which allows deep characterization of vegetative/soil response. Literature presents few papers encompassing UAV-based HS applications in vineyard, a challenging conditions respect to other crops due to high presence of bare soil, grass cover, shadows and high heterogeneity canopy structure with different leaf inclination. The purpose of this paper is to present the first contribution combining traditional and multivariate HS data elaboration techniques, supported by strong ground truthing of vine ecophysiological, vegetative and productive variables. Firstly the research describes the UAV image acquisition and processing workflow to generate a 50 bands HS orthomosaic of a study vineyard. Subsequently, the spectral data extracted from 60 sample vines were elaborated both investigating the relationship between traditional narrowband VIs and grapevine traits. Then, multivariate calibration models were built using a double approach based on Partial Least Square (PLS) regression and interval-PLS (iPLS), to evaluate the correlation performance between the biophysical parameters and HS imagery using the whole spectral range and a selection of more relevant bands applying a variable selection algorithm, respectively. All techniques (VIs, PLS and iPLS) provided satisfactory correlation performances for the ecophysiological (R 2 = 0.65), productive (R 2 = 0.48), and qualitative (R 2 = 0.63) grape parameters. The novelty of this work is represented by the first assessment of a UAV HS dataset with the expression of the entire vine ecosystem, from the physiological and vegetative state to grapes production and quality, using narrowband VIs and multivariate PLS regressions. A correct non-destructive estimation of key parameters in vineyard, above all physiological parameters which must be measured in a short time as they are extremely influenced by the variability of environmental conditions during the day, represents a powerful tool to support the winegrower in vineyard management.
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Affiliation(s)
- Alessandro Matese
- Institute of BioEconomy, National Research Council (CNR-IBE), Firenze, Italy
| | | | - Giorgia Orlandi
- Institute of BioEconomy, National Research Council (CNR-IBE), Firenze, Italy
| | - Matteo Gatti
- Department of Sustainable Crop Production (DI.PRO.VE.S.), Università Cattolica del Sacro Cuore, Piacenza, Italy
| | - Stefano Poni
- Department of Sustainable Crop Production (DI.PRO.VE.S.), Università Cattolica del Sacro Cuore, Piacenza, Italy
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