Predicting Citrus Leaf Nitrogen Content Based on Hybrid Bat Algorithm Optimized PLSR
Hyperspectral imaging technology,characterized by its rapid and non-destructive detection capa-bilities,holds promising application prospects,particularly in agricultural fields such as crop growth moni-toring. Leaf nitrogen content serves as an important indicator for assessing crop growth conditions,which is vital for understanding crop development and establishing precise fertilization strategies. This study fo-cused on exploring the potential application of hyperspectral technology in assessing the nitrogen content of citrus leaves by collecting both hyperspectral data and corresponding nitrogen content data of the leaves. To enhance the preprocessing effect of the hyperspectral data,methods such as Standard Normal Variate Transformation (SNV) and Savitzky-Golay (SG) smoothing filters were utilized to eliminate noise from the spectral data. The Competitive Adaptive Reweighted Sampling (CARS) and Sequential Projection Al-gorithm (SPA) were adopted for screening the featured bands closely associated with leaf nitrogen con-tent,and combined with various models including Partial Least Squares Regression (PLSR),Support Vec-tor Regression (SVR) to optimize the PLSR (O-PLSR) based on Genetic Algorithm (GA) and Bat Algo-rithm (BA) for predicting the leaf nitrogen content. The results indicate that compared to the SVR model,the PLSR model exhibited higher precision. Moreover,optimizing the PLSR model via GA and BA algo-rithms facilitated further enhancement of modelling precision,with the R2 value increasing by up to 14.8％. Notably,the SG-filtered and CARS-optimized PLSR model (SG-CARS-O-PLSR) demonstrated optimal performance in estimating accuracy,with a determination coefficient (R2) of 0.94 and Root Mean Square Error of 0.55. The high precision of the SG-CARS-O-PLSR model underscores the practical value of hyperspectral imaging technology in agriculture,particularly in the monitoring the level of nitrogen in crop. This contributes to the advancement of intelligent orchard management and the implementation of precision agriculture.
citrushyperspectral imaging technologyleaf nitrogen contentpartial least squares regression
万方数据
维普
