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Zang H, Wang Y, Ru L, Zhou M, Chen D, Zhao Q, Zhang J, Li G, Zheng G. Detection method of wheat spike improved YOLOv5s based on the attention mechanism. FRONTIERS IN PLANT SCIENCE 2022; 13:993244. [PMID: 36247573 PMCID: PMC9554473 DOI: 10.3389/fpls.2022.993244] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 08/30/2022] [Indexed: 05/17/2023]
Abstract
In wheat breeding, spike number is a key indicator for evaluating wheat yield, and the timely and accurate acquisition of wheat spike number is of great practical significance for yield prediction. In actual production; the method of using an artificial field survey to count wheat spikes is time-consuming and labor-intensive. Therefore, this paper proposes a method based on YOLOv5s with an improved attention mechanism, which can accurately detect the number of small-scale wheat spikes and better solve the problems of occlusion and cross-overlapping of the wheat spikes. This method introduces an efficient channel attention module (ECA) in the C3 module of the backbone structure of the YOLOv5s network model; at the same time, the global attention mechanism module (GAM) is inserted between the neck structure and the head structure; the attention mechanism can be more Effectively extract feature information and suppress useless information. The result shows that the accuracy of the improved YOLOv5s model reached 71.61% in the task of wheat spike number, which was 4.95% higher than that of the standard YOLOv5s model and had higher counting accuracy. The improved YOLOv5s and YOLOv5m have similar parameters, while RMSE and MEA are reduced by 7.62 and 6.47, respectively, and the performance is better than YOLOv5l. Therefore, the improved YOLOv5s method improves its applicability in complex field environments and provides a technical reference for the automatic identification of wheat spike numbers and yield estimation. Labeled images, source code, and trained models are available at: https://github.com/228384274/improved-yolov5.
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Affiliation(s)
- Hecang Zang
- Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou, China
- Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Zhengzhou, China
| | - Yanjing Wang
- College of Life Sciences, Zhengzhou Normal University, Zhengzhou, China
- *Correspondence: Yanjing Wang,
| | - Linyuan Ru
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, China
| | - Meng Zhou
- Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou, China
- Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Zhengzhou, China
| | - Dandan Chen
- Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou, China
- Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Zhengzhou, China
| | - Qing Zhao
- Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou, China
- Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Zhengzhou, China
| | - Jie Zhang
- Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou, China
- Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Zhengzhou, China
| | - Guoqiang Li
- Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou, China
- Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Zhengzhou, China
- Guoqiang Li,
| | - Guoqing Zheng
- Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou, China
- Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Zhengzhou, China
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Rahman MM, Crain J, Haghighattalab A, Singh RP, Poland J. Improving Wheat Yield Prediction Using Secondary Traits and High-Density Phenotyping Under Heat-Stressed Environments. FRONTIERS IN PLANT SCIENCE 2021; 12:633651. [PMID: 34646280 PMCID: PMC8502926 DOI: 10.3389/fpls.2021.633651] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 08/19/2021] [Indexed: 06/08/2023]
Abstract
A primary selection target for wheat (Triticum aestivum) improvement is grain yield. However, the selection for yield is limited by the extent of field trials, fluctuating environments, and the time needed to obtain multiyear assessments. Secondary traits such as spectral reflectance and canopy temperature (CT), which can be rapidly measured many times throughout the growing season, are frequently correlated with grain yield and could be used for indirect selection in large populations particularly in earlier generations in the breeding cycle prior to replicated yield testing. While proximal sensing data collection is increasingly implemented with high-throughput platforms that provide powerful and affordable information, efficient and effective use of these data is challenging. The objective of this study was to monitor wheat growth and predict grain yield in wheat breeding trials using high-density proximal sensing measurements under extreme terminal heat stress that is common in Bangladesh. Over five growing seasons, we analyzed normalized difference vegetation index (NDVI) and CT measurements collected in elite breeding lines from the International Maize and Wheat Improvement Center at the Regional Agricultural Research Station, Jamalpur, Bangladesh. We explored several variable reduction and regularization techniques followed by using the combined secondary traits to predict grain yield. Across years, grain yield heritability ranged from 0.30 to 0.72, with variable secondary trait heritability (0.0-0.6), while the correlation between grain yield and secondary traits ranged from -0.5 to 0.5. The prediction accuracy was calculated by a cross-fold validation approach as the correlation between observed and predicted grain yield using univariate and multivariate models. We found that the multivariate models resulted in higher prediction accuracies for grain yield than the univariate models. Stepwise regression performed equal to, or better than, other models in predicting grain yield. When incorporating all secondary traits into the models, we obtained high prediction accuracies (0.58-0.68) across the five growing seasons. Our results show that the optimized phenotypic prediction models can leverage secondary traits to deliver accurate predictions of wheat grain yield, allowing breeding programs to make more robust and rapid selections.
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Affiliation(s)
- Mohammad Mokhlesur Rahman
- Department of Plant Pathology, Throckmorton Plant Sciences Center, Kansas State University, Manhattan, KS, United States
| | - Jared Crain
- Department of Plant Pathology, Throckmorton Plant Sciences Center, Kansas State University, Manhattan, KS, United States
| | - Atena Haghighattalab
- Stakman-Borlaug Center for Sustainable Plant Health, University of Minnesota, St Paul, MN, United States
| | - Ravi P. Singh
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Jesse Poland
- Department of Plant Pathology, Wheat Genetics Resource Center, Throckmorton Plant Sciences Center, Kansas State University, Manhattan, KS, United States
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Ghahremani M, Williams K, Corke FMK, Tiddeman B, Liu Y, Doonan JH. Deep Segmentation of Point Clouds of Wheat. FRONTIERS IN PLANT SCIENCE 2021; 12:608732. [PMID: 33841454 PMCID: PMC8025700 DOI: 10.3389/fpls.2021.608732] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 02/24/2021] [Indexed: 05/31/2023]
Abstract
The 3D analysis of plants has become increasingly effective in modeling the relative structure of organs and other traits of interest. In this paper, we introduce a novel pattern-based deep neural network, Pattern-Net, for segmentation of point clouds of wheat. This study is the first to segment the point clouds of wheat into defined organs and to analyse their traits directly in 3D space. Point clouds have no regular grid and thus their segmentation is challenging. Pattern-Net creates a dynamic link among neighbors to seek stable patterns from a 3D point set across several levels of abstraction using the K-nearest neighbor algorithm. To this end, different layers are connected to each other to create complex patterns from the simple ones, strengthen dynamic link propagation, alleviate the vanishing-gradient problem, encourage link reuse and substantially reduce the number of parameters. The proposed deep network is capable of analysing and decomposing unstructured complex point clouds into semantically meaningful parts. Experiments on a wheat dataset verify the effectiveness of our approach for segmentation of wheat in 3D space.
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Affiliation(s)
- Morteza Ghahremani
- National Plant Phenomics Centre, Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
- Department of Computer Science, Aberystwyth University, Aberystwyth, United Kingdom
| | - Kevin Williams
- National Plant Phenomics Centre, Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Fiona M. K. Corke
- National Plant Phenomics Centre, Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Bernard Tiddeman
- Department of Computer Science, Aberystwyth University, Aberystwyth, United Kingdom
| | - Yonghuai Liu
- Department of Computer Science, Edge Hill University, Ormskirk, United Kingdom
| | - John H. Doonan
- National Plant Phenomics Centre, Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
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David E, Serouart M, Smith D, Madec S, Velumani K, Liu S, Wang X, Pinto F, Shafiee S, Tahir ISA, Tsujimoto H, Nasuda S, Zheng B, Kirchgessner N, Aasen H, Hund A, Sadhegi-Tehran P, Nagasawa K, Ishikawa G, Dandrifosse S, Carlier A, Dumont B, Mercatoris B, Evers B, Kuroki K, Wang H, Ishii M, Badhon MA, Pozniak C, LeBauer DS, Lillemo M, Poland J, Chapman S, de Solan B, Baret F, Stavness I, Guo W. Global Wheat Head Detection 2021: An Improved Dataset for Benchmarking Wheat Head Detection Methods. PLANT PHENOMICS (WASHINGTON, D.C.) 2021; 2021:9846158. [PMID: 34778804 PMCID: PMC8548052 DOI: 10.34133/2021/9846158] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 08/11/2021] [Indexed: 05/03/2023]
Abstract
The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition hosted in Kaggle, GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities. From this first experience, a few avenues for improvements have been identified regarding data size, head diversity, and label reliability. To address these issues, the 2020 dataset has been reexamined, relabeled, and complemented by adding 1722 images from 5 additional countries, allowing for 81,553 additional wheat heads. We now release in 2021 a new version of the Global Wheat Head Detection dataset, which is bigger, more diverse, and less noisy than the GWHD_2020 version.
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Affiliation(s)
- Etienne David
- Arvalis, Institut du Végétal, 3 Rue Joseph et Marie Hackin, 75116 Paris, France
- UMR1114 EMMAH, INRAE, Centre PACA, Bâtiment Climat, Domaine Saint-Paul, 228 Route de l'Aérodrome, CS 40509, 84914 Avignon Cedex, France
| | - Mario Serouart
- Arvalis, Institut du Végétal, 3 Rue Joseph et Marie Hackin, 75116 Paris, France
- UMR1114 EMMAH, INRAE, Centre PACA, Bâtiment Climat, Domaine Saint-Paul, 228 Route de l'Aérodrome, CS 40509, 84914 Avignon Cedex, France
| | - Daniel Smith
- School of Food and Agricultural Sciences, The University of Queensland, Gatton, 4343 QLD, Australia
| | - Simon Madec
- Arvalis, Institut du Végétal, 3 Rue Joseph et Marie Hackin, 75116 Paris, France
- School of Food and Agricultural Sciences, The University of Queensland, Gatton, 4343 QLD, Australia
| | - Kaaviya Velumani
- UMR1114 EMMAH, INRAE, Centre PACA, Bâtiment Climat, Domaine Saint-Paul, 228 Route de l'Aérodrome, CS 40509, 84914 Avignon Cedex, France
- Hiphen SAS, 120 Rue Jean Dausset, Agroparc, Bâtiment Technicité, 84140 Avignon, France
| | - Shouyang Liu
- Plant Phenomics Research Center, Nanjing Agricultural University, Nanjing, China
| | - Xu Wang
- Wheat Genetics Resource Center, Dep. of Plant Pathology, Kansas State Univ., 4024 Throckmorton Plant Sciences Center, Manhattan, Kansas, USA
| | - Francisco Pinto
- Global Wheat Program, International Maize and Wheat Improvement Centre (CIMMYT), Mexico, D.F., Mexico
| | - Shahameh Shafiee
- Faculty of Biosciences, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway
| | - Izzat S. A. Tahir
- Agricultural Research Corporation, Wheat Research Program, P.O. Box 126, Wad Medani, Sudan
| | - Hisashi Tsujimoto
- Arid Land Research Center, Tottori University, Tottori 680-0001, Japan
| | - Shuhei Nasuda
- Laboratories of Plant Genetics and Plant Breeding, Graduate School of Agriculture, Kyoto University, Japan
| | - Bangyou Zheng
- CSIRO Agriculture and Food, Queensland Biosciences Precinct, 306 Carmody Road, St Lucia, 4067 QLD, Australia
| | - Norbert Kirchgessner
- Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092 Zurich, Switzerland
| | - Helge Aasen
- Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092 Zurich, Switzerland
| | - Andreas Hund
- Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092 Zurich, Switzerland
| | | | - Koichi Nagasawa
- Institute of Crop Science, National Agriculture and Food Research Organization, Japan
| | - Goro Ishikawa
- Hokkaido Agricultural Research Center, National Agriculture and Food Research Organization, Japan
| | - Sébastien Dandrifosse
- Biosystems Dynamics and Exchanges, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
| | - Alexis Carlier
- Biosystems Dynamics and Exchanges, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
| | - Benjamin Dumont
- Plant Sciences, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
| | - Benoit Mercatoris
- Biosystems Dynamics and Exchanges, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
| | - Byron Evers
- Wheat Genetics Resource Center, Dep. of Plant Pathology, Kansas State Univ., 4024 Throckmorton Plant Sciences Center, Manhattan, Kansas, USA
| | - Ken Kuroki
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Midori-cho, Nishitokyo City, Tokyo, Japan
| | - Haozhou Wang
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Midori-cho, Nishitokyo City, Tokyo, Japan
| | - Masanori Ishii
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Midori-cho, Nishitokyo City, Tokyo, Japan
| | | | - Curtis Pozniak
- Department of Plant Sciences, University of Saskatchewan, Canada
| | - David Shaner LeBauer
- College of Agriculture and Life Sciences, University of Arizona, Tucson, Arizona, USA
| | - Morten Lillemo
- Faculty of Biosciences, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway
| | - Jesse Poland
- Wheat Genetics Resource Center, Dep. of Plant Pathology, Kansas State Univ., 4024 Throckmorton Plant Sciences Center, Manhattan, Kansas, USA
| | - Scott Chapman
- School of Food and Agricultural Sciences, The University of Queensland, Gatton, 4343 QLD, Australia
- CSIRO Agriculture and Food, Queensland Biosciences Precinct, 306 Carmody Road, St Lucia, 4067 QLD, Australia
| | - Benoit de Solan
- Arvalis, Institut du Végétal, 3 Rue Joseph et Marie Hackin, 75116 Paris, France
| | - Frédéric Baret
- UMR1114 EMMAH, INRAE, Centre PACA, Bâtiment Climat, Domaine Saint-Paul, 228 Route de l'Aérodrome, CS 40509, 84914 Avignon Cedex, France
| | - Ian Stavness
- Department of Computer Science, University of Saskatchewan, Canada
| | - Wei Guo
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Midori-cho, Nishitokyo City, Tokyo, Japan
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Hickey LT, N Hafeez A, Robinson H, Jackson SA, Leal-Bertioli SCM, Tester M, Gao C, Godwin ID, Hayes BJ, Wulff BBH. Breeding crops to feed 10 billion. Nat Biotechnol 2019; 37:744-754. [PMID: 31209375 DOI: 10.1038/s41587-019-0152-9] [Citation(s) in RCA: 322] [Impact Index Per Article: 64.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2018] [Accepted: 04/25/2019] [Indexed: 12/14/2022]
Abstract
Crop improvements can help us to meet the challenge of feeding a population of 10 billion, but can we breed better varieties fast enough? Technologies such as genotyping, marker-assisted selection, high-throughput phenotyping, genome editing, genomic selection and de novo domestication could be galvanized by using speed breeding to enable plant breeders to keep pace with a changing environment and ever-increasing human population.
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Affiliation(s)
- Lee T Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland, Australia.
| | | | | | - Scott A Jackson
- Center for Applied Genetic Technologies, Department of Crop and Soil Sciences, University of Georgia, Athens, GA, USA
| | - Soraya C M Leal-Bertioli
- Center for Applied Genetic Technologies, Department of Plant Pathology, University of Georgia, Athens, GA, USA
| | - Mark Tester
- King Abdullah University of Science and Technology (KAUST), Division of Biological and Environmental Sciences and Engineering, Thuwal, Saudi Arabia
| | - Caixia Gao
- State Key Laboratory of Plant Cell and Chromosome Engineering, Center for Genome Editing, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing, China
| | - Ian D Godwin
- School of Agriculture and Food Sciences, The University of Queensland, Brisbane, Queensland, Australia
| | - Ben J Hayes
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland, Australia
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