An exploratory early-assessment framework for crop yield and quality based on multi-source sensor data fusion and explainable machine learning: A case study of rice under low-light stress

Smart Agricultural Technology一手来源 · 原始研究11月1日 · 周日阅读 0

核心看点

探索性研究提出多源传感器融合与可解释机器学习的水稻弱光胁迫早期评估框架，方法有新意但属单案例探索，产业影响有限。

关注点

该框架将多源传感器数据与可解释机器学习结合，用于水稻弱光胁迫下产量与品质的早期评估，适用于设施或可控环境下的苗期预警探索，尚需多品种多区域验证。

发布主体的原始资料；自述数据与结论仍以原文限定为准。

论文信息

Hongbo Wang / Xinyue Sui / Jinwen Wu 等 11 人

Smart Agricultural Technology

DOI: 10.1016/j.atech.2026.102592

论文摘要

An exploratory early-assessment framework for crop yield and quality based on multi-source sensor data fusion and explainable machine learning: A case study of rice under low-light stress。Smart Agricultural Technology

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智慧农业水稻可解释机器学习多源传感器融合弱光胁迫

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