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 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

Bibliographic Details

Main Authors:	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., Iwahashi, Y., Nakajima, K., Takeuchi, E., Saito, Kazuki
Format:	Manuscript-unpublished biblioteca
Language:	English
Published:	Research Square Platform LLC 2021-10-29
Subjects:	crop yield, rice, crop production,
Online Access:	https://hdl.handle.net/10568/125823 https://www.researchsquare.com/article/rs-1026695/v1 https://doi.org/10.21203/rs.3.rs-1026695/v1
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