Early detection of multiple fungal infections in pomegranate fruit using spectral signature analysis and optimized machine learning with hyperspectral-RGB imaging

Computers and Electronics in Agriculture一手来源 · 原始研究11月1日 · 周日阅读 0

核心看点

核心期刊论文，将高光谱-RGB成像与优化机器学习结合用于石榴多种真菌病害早期检测，方法有新颖性但属细分作物场景，产业影响有限。

关注点

该研究用高光谱与RGB融合成像配合优化机器学习实现石榴多种真菌病害的早期识别，可为果园采后分选与无损检测提供技术参考，但结论基于实验样本，田间推广前需验证泛化能力。

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

Seyed Mohamad Javidan / Zhao Zhang / Yiannis Ampatzidis 等 5 人

Computers and Electronics in Agriculture

DOI: 10.1016/j.compag.2026.112622

论文摘要

Early detection of multiple fungal infections in pomegranate fruit using spectral signature analysis and optimized machine learning with hyperspectral-RGB imaging。Computers and Electronics in Agriculture

论文原文与出版信息

Computers and Electronics in Agriculture

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农业人工智能

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智慧农业高光谱成像病害检测石榴

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