Application of Hyperspectral Technology Combined With Bat Algorithm-AdaBoost Model in Field Soil Nutrient Prediction

Abstract

This paper proposes a hyperspectral soil nutrient estimation method based on the bat algorithm (BA)-AdaBoost model. The spectral reflectance, the first derivative of the reflectance, and the reciprocal logarithm of the reflectance are analyzed based on the 800 field soil samples and their hyperspectral data collected. The first derivative of the reciprocal logarithm of the reflectance and the sensitive band was extracted using the correlation coefficient method, and the correlation of the content of soil organic matter, phosphorus, and potassium was solved. The BA is used to optimize the two core parameters of the AdaBoost model (i.e., the maximum number of iterations $(n)$ and the weight reduction coefficient $(v)$ of the weak learner), the classification and regression trees(CART) decision tree is selected as the weak regression learner of the model, and the coefficient of determination is used as parameter optimization. Based on the objective function value, a BA-AdaBoost model was constructed to estimate soil organic matter and phosphorus and potassium contents. The results show that the BA-AdaBoost combined model can better search for globally optimal parameters. The AdaBoost model optimized by BA significantly improved accuracy and reliability. Among the three elements, soil organic matter estimation accuracy is the highest, and the coefficient of determination and the root mean square error are 0.867 and 0.151g $\cdot $ kg−1, respectively. Compared with the model before optimization, the model accuracy and reliability improved by 29.0% and 24.1%, respectively. The results indicate that hyperspectral technology combined with the BA-AdaBoost model has certain application prospects in field soil nutrient estimation.

Keywords

• Hyperspectral
• soil nutrient content
• machine learning
• bat algorithm
• AdaBoost model