Early blight and ladybug beetle infestation are important factors threatening potato yields. The current research on disease classification using the spectral differences between the healthy and disease-stressed leaves of plants has achieved good progress in a variety of crops, but less research has been conducted on early blight in potato. This paper proposes a CARS-SPA-GA feature selection method. First, the raw spectral data of potato leaves in the visible/near-infrared light region were preprocessed. Then, the feature wavelengths were selected via competitive adaptive reweighted sampling (CARS) and the successive projection algorithm (SPA), respectively. Then, the two sets of wavelengths were reorganized and duplicates were removed, and secondary feature selection was conducted with genetic algorithm (GA). Finally, the feature wavelengths were fed into different classifiers and the parameters were optimized using a real-coded genetic algorithm (RCGA). The experimental results show that the feature wavelengths selected by the CARS-SPA-GA method accounted only for 9% of the full band, and the classification accuracy of the RCGA-optimized support vector machine (SVM) classification model reached 98.366%. These results show that it is feasible to classify early blight and ladybug beetle infestation in potato using visible/near-infrared spectral data, and the CARS-SPA-GA method can substantially improve the accuracy and detection efficiency of potato pest and disease classification.

Chlorophyll-a (Chl-a) is an important parameter of water bodies, but due to the complexity of optics in water bodies, it is currently difficult to accurately predict Chl-a concentration in water bodies by traditional methods. In this paper, Sentinel-2 remote sensing images is used as the data source combined with measured data, and Ulansuhai Lake is taken as the study area. An adaptive ant colony exhaustive optimization (A-ACEO) algorithm is proposed for feature selection and combined with a novel intelligent algorithm of optimizing support vector regression (SVR) by genetic algorithm (GA) for prediction of Chl-a concentration. The ant colony optimization (ACO) algorithm is improved to select remote sensing feature bands for Chl-a concentration by introducing relevant optimization strategies. The GA-SVR model is built by optimizing SVR using GA with the selected feature bands as input, and comparing with the traditional SVR model. The simulation results show that under the same conditions, using A-ACEO algorithm to select feature bands as inputs can effectively reduce the model complexity, and improve the model prediction performance, which provides a valuable reference for monitoring Chl-a concentration in lakes.