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.
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