Early detection and lesion visualization of pear leaf anthracnose based on multi-source feature fusion of hyperspectral imaging

Pear anthracnose, caused by Colletotrichum bacteria, is a severe infectious disease that significantly impacts the growth, development, and fruit yield of pear trees. Early detection of pear anthracnose before symptoms manifest is of great importance in preventing its spread and minimizing economic...

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Vydané v:	Frontiers in plant science Ročník 15; s. 1461855
Hlavní autori:	Zhang, Yingying, Li, Xue, Wang, Meiqing, Xu, Tao, Huang, Kai, Sun, Yuanhao, Yuan, Quanchun, Lei, Xiaohui, Qi, Yannan, Lv, Xiaolan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland Frontiers Media SA 08.10.2024 Frontiers Media S.A
Predmet:	Accuracy Adaptive sampling Agronomy Algorithms Anthracnose Artificial neural networks Asymptomatic infection Back propagation networks Cameras Chlorophyll Classification classification model Crop yield Economic impact Fruit trees Fungi Humidity Hyperspectral imaging Infections Infectious diseases Learning algorithms Leaves Lesions Machine learning Maximum likelihood method Medical imaging multi-source features Neural networks Pathogens pear leaves Physiology Plant diseases Plant Science Support vector machines Vegetation index Vision systems Visualization Wavelengths China anthracnose visualization hyperspectral imaging pear leaves classification model multi-source features
ISSN:	1664-462X, 1664-462X
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