Deep learning-based methods for phenotypic trait extraction in rice panicles.

Abstract

Introduction Key rice panicle traits (grain number, panicle length, grain dimensions, maturity) determine yield and quality, and high-precision/high-throughput measurement is critical for rice breeding. Traditional methods are. Methods A dataset of 5300 rice panicle images (loose/normal/dense types; milk/dough/full maturity/over-ripe stages) was constructed, with 3290 for training, 940 for validation, and 470 for testing. A deep learning pipeline integrating. Results The panicle length extraction achieved R²=0.9583, RMSE=5.69 mm. Grain counting R² values were 0.9799 (loose), 0.9551 (normal), 0.9278 (dense). Grain length R²=0.8823, grain width MAPE=6.64%. OPG-YOLOv8. Discussion This study provides a comprehensive, automated tool for rice panicle phenotyping, addressing occlusion challenges and bridging the gap between advanced models and breeding applications.

Plant phenotyping relevance

イネ穂の画像から粒数・穂長・粒形などの形質を抽出する深層学習パイプラインを開発・評価しており、フェノタイピング手法が研究の中心です。

Code and data availability

The supplied blocks describe a 5,300-image rice panicle dataset and an OPG-YOLOv8 pipeline, but contain no public deposit, repository, or availability URL for the images, annotations, code, or trained models. The Data availability statement is listed in the outline but its text is not present, and no authors' public链接/

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