ID | 66550 |
Sort Key | 9
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FullText URL | |
Author |
Tanaka, Yu
Graduate School of Environmental, Life, Natural Science and Technology, Okayama University
Watanabe, Tomoya
Graduate School of Mathematics, Kyushu University
Katsura, Keisuke
Graduate School of Agriculture, Tokyo University of Agriculture and Technology
Tsujimoto, Yasuhiro
Japan International Research Center for Agricultural Sciences
Takai, Toshiyuki
Japan International Research Center for Agricultural Sciences
Tanaka, Takashi Sonam Tashi
Faculty of Biological Sciences, Gifu UniversityFaculty of Biological Sciences, Gifu University
Kawamura, Kensuke
Japan International Research Center for Agricultural Sciences
Saito, Hiroki
Tropical Agriculture Research Front, Japan International Research Center for Agricultural Sciences
Homma, Koki
Graduate School of Agricultural Science, Tohoku University
Mairoua, Salifou Goube
Africa Rice Center (AfricaRice)
Ahouanton, Kokou
Africa Rice Center (AfricaRice)
Ibrahim, Ali
Africa Rice Center (AfricaRice), Regional Station for the Sahel
Senthilkumar, Kalimuthu
Africa Rice Center (AfricaRice)
Semwal, Vimal Kumar
Africa Rice Center (AfricaRice), Nigeria Station
Matute, Eduardo Jose Graterol
Latin American Fund for Irrigated Rice - The Alliance of Bioversity International and CIAT
Corredor, Edgar
Latin American Fund for Irrigated Rice - The Alliance of Bioversity International and CIAT
El-Namaky, Raafat
Rice Research and Training Center, Field Crops Research Institute
Manigbas, Norvie
Philippine Rice Research Institute (PhilRice)
Quilang, Eduardo Jimmy P.
Philippine Rice Research Institute (PhilRice)
Iwahashi, Yu
Graduate School of Agriculture, Kyoto University
Nakajima, Kota
Graduate School of Agriculture, Kyoto University
Takeuchi, Eisuke
Graduate School of Agriculture, Kyoto University
Saito, Kazuki
Japan International Research Center for Agricultural Sciences
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Abstract | Rice (Oryza sativa L.) is one of the most important cereals, which provides 20% of the world’s food energy. However, its productivity is poorly assessed especially in the global South. Here, we provide a first study to perform a deep learning-based approach for instantaneously estimating rice yield using RGB images. During ripening stage and at harvest, over 22,000 digital images were captured vertically downwards over the rice canopy from a distance of 0.8 to 0.9m at 4,820 harvesting plots having the yield of 0.1 to 16.1 t ha-1 across six countries in Africa and Japan. A convolutional neural network (CNN) applied to these data at harvest predicted 68% variation in yield with a relative root mean square error (rRMSE) of 0.22. Even when the resolution of images was reduced (from 0.2 to 3.2cm pixel-1 of ground sampling distance), the model could predict 57% variation in yield, implying that this approach can be scaled by use of unmanned aerial vehicles. Our work offers low-cost, hands-on, and rapid approach for high throughput phenotyping, and can lead to impact assessment of productivity-enhancing interventions, detection of fields where these are needed to sustainably increase crop production.
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Keywords | Rice (Oryza sativa L.)
rough grain yield
convolutional neural network
RGB images
UAV
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Note | 総合論文 (Comprehensive paper)
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Publication Title |
Scientific Reports of the Faculty of Agriculture, Okayama University
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Published Date | 2024-02-01
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Volume | volume113
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Publisher | 岡山大学農学部
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Publisher Alternative | The Faculty of Agriculture, Okayama University
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Start Page | 41
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End Page | 48
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ISSN | 2186-7755
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Content Type |
Departmental Bulletin Paper
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OAI-PMH Set |
岡山大学
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language |
English
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File Version | publisher
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Eprints Journal Name | srfa
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