Estimation of Leaf Chlorophyll Content of Maize from Hyperspectral Data Using E2D-COS Feature Selection, Deep Neural Network, and Transfer Learning

Estimation of Leaf Chlorophyll Content of Maize from Hyperspectral Data Using E2D-COS Feature Selection, Deep Neural Network, and Transfer Learning

Leaf chlorophyll content (LCC) serves as a vital biochemical indicator of photosynthetic activity and nitrogen status, critical for precision agriculture to optimize crop management. While UAV-based hyperspectral sensing offers maize LCC estimation potential, current methods struggle with overlappin...

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Bibliographic Details
Main Authors: Riqiang Chen, Lipeng Ren, Guijun Yang, Zhida Cheng, Dan Zhao, Chengjian Zhang, Haikuan Feng, Haitang Hu, Hao Yang
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Agriculture
Subjects:
maize
chlorophyll
radiative transfer model
feature selection
transfer learning
Online Access:https://www.mdpi.com/2077-0472/15/10/1072
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