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April 2024

Revolutionizing rice yield prediction: Groundbreaking study utilizes deep learning for sustainable agriculture


The global database of the rice canopy image and corresponding rough grain yield.

The increasing global demand for staple crops, projected to rise by 60% by 2050, is a pressing issue intensified by population growth, income increases, and biofuel usage. To meet this demand sustainably, intensifying existing cropland to minimize environmental impacts and yield gaps is critical. Traditional methods like self-reporting and crop cutting are either inaccurate or impractical, and while remote sensing technologies offer potential, their application remains limited in these regions. The advent of machine learning, specifically , deep learning with convolutional neural networks (CNNs), presents a promising avenue. This technology has shown proficiency in image analysis, yet its application in diverse agricultural contexts, especially for versatile crop yield estimation across various environments and cultivars, is unexplored.

In July 2023, Plant Phenomics published a research article titled “Deep learning enables instant and versatile estimation of rice yield using ground-based RGB images “.

This research utilized a multinational database of 4,820 harvested rice plots from 7 countries, encompassing 22,067 images. These images, capturing diverse rice production systems, cultivars, and management practices, were linked to corresponding rough and filled grain yields and aboveground dry weight. Notably, in this study a Convolutional Neural Network (CNN) model was developed to estimate rough grain yield from these images. The CNN structure featured multiple convolutional layers, pooling layers, and activation functions, including ReLU, ELU, and LeakyReLU. This optimized model, after testing different learning rates and batch sizes, explained 69% and 68% of yield variation for validation and test data, respectively. The model’s accuracy was further affirmed by its close match to the observed yields, with cultivars having more data in the training set showing less deviation in yield estimation. The model’s robustness was tested under various conditions: different shooting angles, varying times of day, and dates during the ripening stage. A crucial aspect of the study was understanding the interpretation of images by the CNN model for yield estimation. Techniques like occlusion-based visualization highlighted the importance of panicles in yield estimation. This was further corroborated by a panicle removal experiment, which showed a gradual decrease in estimated yield with the removal of panicles. The model’s practical applications are manifold, offering rapid, accurate yield estimation compared to traditional estimation methods. This is particularly relevant for high-throughput phenotyping and on-station agronomic experiments. However, the study acknowledges limitations, such as the model’s reduced accuracy with lower image resolutions and its current dataset not encompassing various stress conditions.

In conclusion, this study represents a significant advancement in using ground-based RGB images for accurate rice yield prediction. It opens avenues for efficient field management, informed policy decisions, and high-throughput phenotyping, potentially revolutionizing agricultural practices and sustainability.




Yu  Tanaka1,2*†, Tomoya  Watanabe3, Keisuke  Katsura4, Yasuhiro  Tsujimoto5, Toshiyuki  Takai5, Takashi Sonam Tashi Tanaka6,7, Kensuke  Kawamura5, Hiroki  Saito8, Koki  Homma9, Salifou Goube  Mairoua10, Kokou  Ahouanton10, Ali Ibrahim11, Kalimuthu  Senthilkumar12, Vimal Kumar  Semwal13,  Eduardo Jose Graterol  Matute14, Edgar  Corredor14, Raafat El-Namaky15, Norvie  Manigbas16, Eduardo Jimmy P.  Quilang16, Yu  Iwahashi1,  Kota  Nakajima1, Eisuke  Takeuchi1, and Kazuki  Saito5,10,17*†


1Graduate  School  of  Agriculture,  Kyoto  University,  Kitashirakawa  Oiwake-chou,  Sakyo-ku,  Kyoto  606-8502,  Japan.

2Graduate  School  of  Environmental,  Life,  Natural  Science  and  Technology,  Okayama  University,  1-1-1,  Tsushima Naka, Okayama 700-8530, Japan.

3Graduate School of Mathematics, Kyushu University, 744, Motooka, Fukuoka Shi Nishi Ku, Fukuoka 819-0395, Japan.

4Graduate School of Agriculture, Tokyo University of Agriculture and  Technology,  3-5-8  Saiwaicho,  Fuchu,  Tokyo  183-8509,  Japan.  

5Japan  International  Research  Center  for  Agricultural Sciences, 1-1 Ohwashi, Tsukuba, Ibaraki 305-8686, Japan.

6Faculty of Applied Biological Sciences, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan.

7Artificial Intelligence Advanced Research Center, Gifu University, 1-1  Yanagido,  Gifu  501-1193,  Japan.  

8Tropical  Agriculture  Research  Front,  Japan  International  Research  Center  for Agricultural Sciences, 1091-1 Maezato, Ishigaki, Okinawa 907-0002, Japan.

9Graduate School of Agricultural Science,  Tohoku  University,  Aramaki  Aza-Aoba,  Aoba,  Sendai,  Miyagi  980-8572,  Japan.  

10Africa  Rice  Center  (AfricaRice), 01 BP 2551 Bouaké, Côte d’Ivoire.

11Africa Rice Center (AfricaRice), Regional Station for the Sahel, B.P. 96, Saint-Louis, Senegal.

12Africa Rice Center (AfricaRice), P.O. Box 1690, Ampandrianomby, Antananarivo, Madagascar.

13Africa  Rice  Center  (AfricaRice),  Nigeria  Station,  c/o  IITA,  PMB  5320,  Ibadan,  Nigeria.  

14Latin American Fund for Irrigated Rice – The Alliance of Bioversity International and CIAT, Km 17 Recta Cali-Palmira, C.P.  763537,  A.A.  6713,  Cali,  Colombia.  

15Rice  Research  and  Training  Center,  Field  Crops  Research  Institute,  ARC, Giza, Egypt.

16Philippine  Rice  Research  Institute  (PhilRice),  Maligaya,  Science  City  of  Muñoz,  3119  Nueva  Ecija, Philippines.

17International Rice Research Institute (IRRI), DAPO Box 7777, Metro Manila 1301, Philippines.

About Yu Tanaka & Kazuki Saito

Yu Tanaka: He is an associate professor at the Graduate  School  of  Agriculture, Kyoto  University.

Kazuki Saito: He is a Principal Scientist at Africa Rice Center (AfricaRice). He has made significant contributions to rice breeding projects in Africa by discovering new breeding materials, developing selection methods, and conducting other research to improve food self-sufficiency in Africa. He was also recognized for his significant achievements in improving and consolidating rice farming systems for small-scale farmers and improving farmers’ livelihoods and nutrition. QR Code

Published Date: December 20, 2023

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