Robustness of the RGB image-based estimation for rice above-ground biomass by utilizing the dataset collected across multiple locations

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dc.creatorNakajima, Kota
dc.creatorSaito, Kazuki
dc.creatorTsujimoto, Yasuhiro
dc.creatorTakai, Toshiyuki
dc.creatorMochizuki, Atsushi
dc.creatorYamaguchi, Tomoaki
dc.creatorIbrahim, Ali
dc.creatorMairoua, Salifou Goube
dc.creatorAndrianary, Bruce Haja
dc.creatorKatsura, Keisuke
dc.creatorTanaka, Yu
dc.date2025-08
dc.date2025-08-04T03:02:18Z
dc.date2025-08-04T03:02:18Z
dc.date.accessioned2026-06-27T04:12:14Z
dc.descriptionAbove-ground biomass (AGB) is a critical phenotype representing crop growth. Non-invasive evaluations of AGB, including deep-learning-based red-green-blue (RGB) image analyses, are often specific to the training data. The robustness of the estimation model across untrained conditions is essential to monitor crop productivity globally, but it has yet to be fully assessed. This study aims to assess the robustness of a convolutional neural network (CNN) model for rice AGB estimation across five locations in three countries, and to demonstrate the feasibility of robust model via a practical approach. From transplanting to heading, 1957 RGB images were captured vertically downward over the rice canopy, covering approximately 1 m2. First, a base model was established using data collected from a single location. Then, its robustness was assessed using test datasets taken from the other four locations. The CNN model showed a significant variation in estimation accuracy across the untrained four locations, indicating insufficient robustness of the base model. Subsequently, we quantitatively tested the impact of improving training data diversity on model robustness by adding data from each of the four locations to the base model's training data. Adding at most 48 data points from a location achieved practical accuracy for the added location, with R2Adabove 0.8. Interestingly, adding data from one location sometimes improved the accuracy for other untrained locations as well. These findings suggest that collecting diverse training data for RGB-based estimation, combined with evaluation of robustness paves the way for on-site and instant AGB monitoring of rice.
dc.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/175950
dc.identifier.urihttp://hdl.handle.net/123456789/24974
dc.languageen
dc.publisherElsevier
dc.rightsOpen Access
dc.sourceNakajima, Kota, Kazuki Saito, Yasuhiro Tsujimoto, Toshiyuki Takai, Atsushi Mochizuki, Tomoaki Yamaguchi, Ali Ibrahim et al. "Robustness of the RGB image-based estimation for rice above-ground biomass by utilizing the dataset collected across multiple locations." Smart Agricultural Technology 11 (2025): 100998.
dc.subjectabove ground biomass
dc.subjectcrop growth
dc.subjectcrop monitoring
dc.subjectdata collection
dc.subjectfield experiments
dc.subjecttransplanting
dc.subjectmachine learning
dc.subjectremote sensing
dc.titleRobustness of the RGB image-based estimation for rice above-ground biomass by utilizing the dataset collected across multiple locations
dc.typeJournal Article

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Instituto Internacional de Investigación del Arroz (IRRI)