Machine learning assisted analysis of rice flower opening times using a low-cost time-lapse camera.

Abstract

Flower opening time (FOT) is a key trait for successful reproduction and reproductive isolation. In crop science, FOT is critical for stress avoidance and efficient breeding practices. This study developed a system for the automatic detection of rice flower openings and FOT estimation by integrating a low-cost time-lapse camera with machine learning technology. This approach enabled high-resolution monitoring of flowering dynamics in two cultivars: the japonica cultivar Taichung 65 (T65) and the indica cultivar IR24. The system accurately identified regions containing open flowers, and the estimated FOTs varied within a 3-h range, with a root mean square error of approximately 30 min compared to manual detection. A significant difference in estimated FOTs between IR24 and T65 demonstrated the system's potential for genetic screening applications. FOT of both cultivars exhibited a significant negative correlation with daily mean temperature. Notably, a temperature-sensitive period was identified in the morning, suggesting that temperature influences not only flower opening but also preceding physiological processes such as panicle and spikelet development. This study presents a novel approach to investigating FOT dynamics in rice and provides insights into the interaction between environmental factors and internal regulatory mechanisms governing this critical reproductive trait.

Plant phenotyping relevance

低コストタイムラプスカメラと機械学習によるイネの開花時刻という植物形質の自動検出・推定システムを開発し、手動検出との誤差で検証しているため、方法が中心的です。

Code and data availability

The authors explicitly state that the Python scripts, training dataset (annotated time-lapse rice flower images), and trained YOLOX model used for flower-opening detection are publicly available on their GitHub repository (mwbotan/FLpanicle). This is a paper-specific, public, actionable asset directly reproducing the F