Open AccessAnalysis on Chlorophyll Diagnosis of Wheat Leaves Based on Digital Image Processing and Feature Selection
Author(s) -
YuFei Song,
Shiwu Li,
Zhiguo Liu,
Yuekui Zhang,
Nan Shen
Publication year - 2022
Publication title -
traitement du signal/ts. traitement du signal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.279
H-Index - 11
eISSN - 1958-5608
pISSN - 0765-0019
DOI - 10.18280/ts.390140
Subject(s) - feature selection , selection (genetic algorithm) , mean squared error , chlorophyll , mathematics , agronomy , computer science , artificial intelligence , pattern recognition (psychology) , statistics , horticulture , biology
Crop nutrition measurement is of great significance in agricultural practice, especially in variable rate fertilization. The chlorophyll content, an important indicator of nitrogen nutrition in crops, largely depends on crop growth and development, photosynthesis, and crop yield, and plays an important role in the monitoring of crop growth. This paper tries to detect the chlorophyll content of wheat quickly, using the digital image processing technology. Specifically, a feature selection method was developed based on wrapper and light gradient boosting machine (LGBM), and combined with logistic regression (LR) to predict the chlorophyll content of wheat. The results show that: the optimal model is the combination between the 17 image evaluation indices screened by LGBM and the LR prediction model; the optimal results were coefficient of determination (R2) of 0.728, and root mean square error (RMSE) of 4.979. The optimal model can predict the chlorophyll content of wheat accurately based on digital images in field prototype.