Nitrogen Content Estimation of Apple Leaves Using Hyperspectral Analysis

Leaf nitrogen content (LNC) is an important factor reflecting the growth quality of plants. We estimated the nitrogen content of apple leaves using hyperspectral wavelength analysis using the differential spectrum, differential spectrum transformation, and vegetation spectrum index with different derivative gaps. We then used the characteristic wavelengths extracted via the correlation coefficient method as the input vectors to the gradient boosting decision tree (GBDT) model for analysis and performed cross-validation to optimize the inversion model parameters. We analyzed the results with different input variables and loss functions and compared the GBDT model with other mainstream algorithm models. The results show that the R 2 value of the optimized GBDT inversion model is higher than that obtained using the random forest (RF) and support vector regression (SVR) models. Thus, the GBDT model is accurate, and the characteristic wavelength analysis is helpful for the tasks of real-time monitoring and detection of apple tree health.