Estimation of Winter Wheat Canopy Chlorophyll Content Based on Canopy Spectral Transformation and Machine Learning Method

• Published in: Agronomy (Basel) Vol. 13; no. 3; p. 783
• Main Authors: Chen, Xiaokai, Li, Fenling, Shi, Botai, Fan, Kai, Li, Zhenfa, Chang, Qingrui
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2023
• Subjects: Accuracy; Agriculture; agronomy; Calibration; Canopies; canopy; canopy chlorophyll content; canopy spectral transformation; China; Chlorophyll; Correlation coefficient; Correlation coefficients; Crop growth; Crops; data collection; Decision trees; Estimation; Fertilizers; Genetic transformation; hyperspectral remote sensing; Independent variables; leaf reflectance; Learning algorithms; Leaves; Machine learning; Methods; narrow-band spectral index; Nitrogen; Pests; Photosynthesis; Plant pigments; Potassium; Precision agriculture; prediction; Prediction models; Reflectance; Regression; Regression analysis; Remote sensing; Seasons; Triticum aestivum; Vegetation; Wheat; Winter; Winter wheat; China; Shaanxi China

Canopy chlorophyll content (CCC) is closely related to crop nitrogen status, crop growth and productivity, detection of diseases and pests, and final yield. Thus, accurate monitoring of chlorophyll content in crops is of great significance for decision support in precision agriculture. In this study...