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 • 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.

A Study on the Optimal Design of PMa-SynRM for Electric Vehicles Combining Random Forest and Genetic Algorithm

“…RF performance is validated using the same test functions as the Lasso regressor [30], [31], Support vector regressor (SVR) [32], [33], and AdaBoost regressor [34], [35], [36]. The comparison results are shown in Table 2.…”

Deep residual network for soil nutrient assessment using optical sensors

“…Wang et al. (2022) developed a hyperspectral soil nutrient evaluation approach on the basis of the BA‐AdaBoost approach. The first derivative of reflectance, spectral reflectance, as well as reciprocal logarithm of the reflectance was examined on the basis of 800 field soil samples as well as their hyperspectral data gathered.…”

Fast terminal sliding mode control of agricultural robots with permanent magnet synchronous motor servo systems based on an extended state observer for path tracking

“…15,16 PMSM servo systems frequently employ conventional Proportional Integral Derivative (PID) control, which lacks precision and is ARTICLE pubs.aip.org/aip/adv susceptible to variations in internal parameters and external disturbances, making it challenging to achieve high-performance control in servo systems. 17,18 Sliding mode control (SMC) is a robust control method that is less affected by external disturbances and internal parameter variations, garnering significant attention in PMSM servo systems. 19,20 However, the chattering phenomenon, caused by the discontinuous switching term inherent in SMC, compromises control performance and introduces system vibrations, hindering practical applications.…”