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タイトル: | Deep learning enables instant and versatile estimation of rice yield using ground-based RGB images |
著者: | Tanaka, Yu Watanabe, Tomoya Katsura, Keisuke Tsujimoto, Yasuhiro Takai, Toshiyuki Tanaka, Takashi Sonam Tashi Kawamura, Kensuke Saito, Hiroki Homma, Koki Mairoua, Salifou Goube Ahouanton, Kokou Ibrahim, Ali Senthilkumar, Kalimuthu Semwal, Vimal Kumar Matute, Edurado Graterol Corredor, Edgar El-Namaky, Raafat Manigbas, Norbie L. Quilang, Edurado Jimmy P. Iwahashi, Yu https://orcid.org/0000-0002-1291-6329 (unconfirmed) Nakajima, Kota Takeuchi, Eisuke Saito, Kazuki |
著者名の別形: | 田中, 佑 渡邊, 智也 桂, 圭佑 辻本, 泰弘 高井, 俊之 田中, 貴 川村, 健介 齊藤, 大樹 本間, 香貴 岩橋, 優 中嶌, 洸太 竹内, 瑛祐 齋藤, 和樹 |
発行日: | 28-Jul-2023 |
出版者: | American Association for the Advancement of Science (AAAS) |
誌名: | Plant Phenomics |
巻: | 5 |
論文番号: | 0073 |
抄録: | 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 red-green-blue images. During ripening stage and at harvest, over 22, 000 digital images were captured vertically downward over the rice canopy from a distance of 0.8 to 0.9 m at 4, 820 harvesting plots having the yield of 0.1 to 16.1 t·ha⁻¹ across 6 countries in Africa and Japan. A convolutional neural network applied to these data at harvest predicted 68% variation in yield with a relative root mean square error of 0.22. The developed model successfully detected genotypic difference and impact of agronomic interventions on yield in the independent dataset. The model also demonstrated robustness against the images acquired at different shooting angles up to 30° from right angle, diverse light environments, and shooting date during late ripening stage. Even when the resolution of images was reduced (from 0.2 to 3.2 cm·pixel−1 of ground sampling distance), the model could predict 57% variation in yield, implying that this approach can be scaled by the 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, and yield forecast at several weeks before harvesting. |
記述: | "AIの目"によるイネ収穫量の簡単・迅速推定. 京都大学プレスリリース. 2023-07-21. |
著作権等: | Copyright © 2023 Yu Tanaka et al. Exclusive licensee Nanjing Agricultural University. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0). |
URI: | http://hdl.handle.net/2433/284495 |
DOI(出版社版): | 10.34133/plantphenomics.0073 |
関連リンク: | https://www.kyoto-u.ac.jp/ja/research-news/2023-07-21 |
出現コレクション: | 学術雑誌掲載論文等 |
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