Using Machine Learning for Estimating Rice Chlorophyll Content from In Situ Hyperspectral Data

Using Machine Learning for Estimating Rice Chlorophyll Content from In Situ Hyperspectral Data

Chlorophyll is an essential pigment for photosynthesis in crops, and leaf chlorophyll content can be used as an indicator for crop growth status and help guide nitrogen fertilizer applications. Estimating crop chlorophyll content plays an important role in precision agriculture. In this study, a var...

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Bibliographic Details
Main Authors: Gangqiang An, Minfeng Xing, Binbin He, Chunhua Liao, Xiaodong Huang, Jiali Shang, Haiqi Kang
Format: Article
Language:English
Published: MDPI AG 2020-09-01
Series:Remote Sensing
Subjects:
hyperspectral remote sensing
machine learning technology
RCRW<sub>a-b</sub>
SPAD value
rice
Online Access:https://www.mdpi.com/2072-4292/12/18/3104

Internet

https://www.mdpi.com/2072-4292/12/18/3104

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