Deep learning-based estimation of rice yield using RGB image
Crop productivity is poorly assessed globally. Here, we provide a deep learning-based approach for 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 0.9 m, and r...
| Autores principales: | , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Lenguaje: | Inglés |
| Publicado: |
Research Square Platform LLC
2021
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/125823 |
| _version_ | 1855525221131354112 |
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| author | Tanaka, Y Watanabe, T. Katsura, K. Tsujimoto, Y. Takai, T. Tanaka, T. Kawamura, K. Saito, H. Homma, K. Mairoua, S. Ahouanton, K. Ibrahim, A. Senthilkumar, Kalimuthu Semwal, V. Corredor, E. El-Namaky, R. Manigbas,N. Quilang, E.J.P. Iwahashi, Y. Nakajima, K. Takeuchi, E. Saito, Kazuki |
| author_browse | Ahouanton, K. Corredor, E. El-Namaky, R. Homma, K. Ibrahim, A. Iwahashi, Y. Katsura, K. Kawamura, K. Mairoua, S. Manigbas,N. Nakajima, K. Quilang, E.J.P. Saito, H. Saito, Kazuki Semwal, V. Senthilkumar, Kalimuthu Takai, T. Takeuchi, E. Tanaka, T. Tanaka, Y Tsujimoto, Y. Watanabe, T. |
| author_facet | Tanaka, Y Watanabe, T. Katsura, K. Tsujimoto, Y. Takai, T. Tanaka, T. Kawamura, K. Saito, H. Homma, K. Mairoua, S. Ahouanton, K. Ibrahim, A. Senthilkumar, Kalimuthu Semwal, V. Corredor, E. El-Namaky, R. Manigbas,N. Quilang, E.J.P. Iwahashi, Y. Nakajima, K. Takeuchi, E. Saito, Kazuki |
| author_sort | Tanaka, Y |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Crop productivity is poorly assessed globally. Here, we provide a deep learning-based approach for
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 0.9 m, and rice yields
were obtained in the corresponding area ranging from 0.1 and 16.1 t ha
−1
. A convolutional neural network
(CNN) applied to these data at harvest predicted 70% variation in rice yield with a relative root mean
square error (rRMSE) of 0.22. Images obtained during the ripening stage can also be used to forecast the
final rice yield. Our work suggests that this low-cost, hands-on, and rapid approach can provide a
breakthrough solution to assess the impact of productivity-enhancing interventions and identify fields
where these are needed to sustainably increase crop production |
| format | Preprint |
| id | CGSpace125823 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Research Square Platform LLC |
| publisherStr | Research Square Platform LLC |
| record_format | dspace |
| spelling | CGSpace1258232024-11-13T09:00:15Z Deep learning-based estimation of rice yield using RGB image Tanaka, Y Watanabe, T. Katsura, K. Tsujimoto, Y. Takai, T. Tanaka, T. Kawamura, K. Saito, H. Homma, K. Mairoua, S. Ahouanton, K. Ibrahim, A. Senthilkumar, Kalimuthu Semwal, V. Corredor, E. El-Namaky, R. Manigbas,N. Quilang, E.J.P. Iwahashi, Y. Nakajima, K. Takeuchi, E. Saito, Kazuki crop yield rice crop production Crop productivity is poorly assessed globally. Here, we provide a deep learning-based approach for 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 0.9 m, and rice yields were obtained in the corresponding area ranging from 0.1 and 16.1 t ha −1 . A convolutional neural network (CNN) applied to these data at harvest predicted 70% variation in rice yield with a relative root mean square error (rRMSE) of 0.22. Images obtained during the ripening stage can also be used to forecast the final rice yield. Our work suggests that this low-cost, hands-on, and rapid approach can provide a breakthrough solution to assess the impact of productivity-enhancing interventions and identify fields where these are needed to sustainably increase crop production 2021-10-29 2022-12-07T08:57:15Z 2022-12-07T08:57:15Z Preprint https://hdl.handle.net/10568/125823 en Open Access Research Square Platform LLC Tanaka, Y., Watanabe, T., Katsura, K., Tsujimoto, Y., Takai, T., Tanaka, T., Kawamura, K., Saito, H., Homma, K., Mairoua, S., Ahouanton, K., Ibrahim, A., Senthilkumar, K., Semwal, V., Corredor, E., El-Namaky, R., Manigbas, N., Quilang, E., Iwahashi, Y., Nakajima, K., Takeuchi, E. and Saito, K. 2021. Deep learning-based estimation of rice yield using, RGB image. Preprint |
| spellingShingle | crop yield rice crop production Tanaka, Y Watanabe, T. Katsura, K. Tsujimoto, Y. Takai, T. Tanaka, T. Kawamura, K. Saito, H. Homma, K. Mairoua, S. Ahouanton, K. Ibrahim, A. Senthilkumar, Kalimuthu Semwal, V. Corredor, E. El-Namaky, R. Manigbas,N. Quilang, E.J.P. Iwahashi, Y. Nakajima, K. Takeuchi, E. Saito, Kazuki Deep learning-based estimation of rice yield using RGB image |
| title | Deep learning-based estimation of rice yield using RGB image |
| title_full | Deep learning-based estimation of rice yield using RGB image |
| title_fullStr | Deep learning-based estimation of rice yield using RGB image |
| title_full_unstemmed | Deep learning-based estimation of rice yield using RGB image |
| title_short | Deep learning-based estimation of rice yield using RGB image |
| title_sort | deep learning based estimation of rice yield using rgb image |
| topic | crop yield rice crop production |
| url | https://hdl.handle.net/10568/125823 |
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