Fusion of Feature Selection Methods and Regression Algorithms for Predicting the Canopy Water Content of Rice Based on Hyperspectral Data

Fusion of Feature Selection Methods and Regression Algorithms for Predicting the Canopy Water Content of Rice Based on Hyperspectral Data

Estimation of the canopy water content (CWC) is extremely important for irrigation management decisions. Machine learning and hyperspectral imaging technology have provided a potentially useful tool for precise measurement of plant water content. The tools, however, are hampered by feature selection...

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Bibliographic Details
Main Authors: Osama Elsherbiny, Yangyang Fan, Lei Zhou, Zhengjun Qiu
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:Agriculture
Subjects:
water content
hyperspectral imaging
feature combination
feature selection
optimal predictive model
Online Access:https://www.mdpi.com/2077-0472/11/1/51

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