Deep learning-based semantic segmentation for rice yield estimation by analyzing the dynamic change of panicle coverage.

Abstract

Introduction Rising global populations and climate change necessitate increased agricultural productivity. Most studies on rice panicle detection using imaging technologies rely on single-time-point analyses, failing to capture the dynamic changes in panicle coverage and their effects on yield. Therefore, this study presents a novel temporal framework for rice phenotyping and yield prediction by integrating high-resolution RGB imagery with deep learning-based semantic segmentation. Methods High-resolution RGB images of rice canopies were acquired over two growing seasons. We evaluated five semantic segmentation models (DeepLabv3+, U-Net, PSPNet, FPN, LinkNet) to effectively delineate rice panicles. Time-series panicle coverage data, extracted from the segmented images, were fitted to a piecewise function to model their growth and decline dynamics. This process distilled key predictive parameters: K (maximum panicle coverage), g (growth rate), d0 (time of maximum growth rate), a (decline rate), and d1 (transition point). These parameters served as predictors in four machine learning regression models (PLSR, RFR, GBR, and XGBR) to estimate yield and its components. Results In panicle segmentation, DeepLabv3+ and LinkNet achieved superior performance (mIoU > 0.81). Among the piecewise function parameters, K showed the strongest positive correlation with Yield and Grain Number (GN) ( r = 0.87 and r = 0.85, respectively), while d0 was strongly negatively correlated with the Filled Grain Ratio (FGR) ( r = -0.71). For yield prediction, the RFR and XGBR models demonstrated the highest performance (R 2 = 0.89). SHAP analysis quantified the relative importance of each parameter for predicting yield components. Discussion This framework proves to be a powerful tool for quantifying rice developmental dynamics and accurately predicting yield using readily available RGB imagery. It holds significant potential for advancing both precision agriculture and crop breeding efforts.

Plant phenotyping relevance

RGB画像と深層学習セグメンテーションによりイネ穂の被覆率を時系列で抽出し、成長動態と収量を推定するフェノタイピング手法が中心である。

Code and data availability

The article describes rice canopy RGB imagery, semantic segmentation models, and yield prediction, but no public phenotype dataset, image collection, analysis code, or trained model checkpoint is deposited. The Data availability statement section is present in the outline but no deposit language or repository URL for a

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