Classification and feature band extraction of diseased citrus plants based on UAV hyperspectral remote sensing

Combined with the advantages and disadvantages of traditional and modern agricultural pest monitoring, the method of monitoring pest and disease were discussed, which detected the diseased citrus plants by UAV hyperspectral remote sensing technology and judged the disease species and disease degree by artificial field investigation.

The original hyperspectral images were obtained by UAV. After spectral preprocessing and feature engineering, continuous projection algorithm was used to extract the feature wavelength combination which contributed the most to the classification of citrus diseased plants. Finally, the BP neural network and XgBoost algorithm were used based on the full band, and the logistic regression and support vector machine algorithm were used to establish the classification model based on the characteristic band.

The AUC scores of BP neural network and XgBoost were 0.8830 and 0.9120 respectively, and the accuracy rates of both methods were over 95%. The feature wavelength combination of 698 and 762 nm was extracted. Based on this characteristic band, the recall rates of logistic regression and support vector machine algorithm were 93.00% and 96.00% respectively.

The model based on characteristic band shows high accuracy in the classification of disease samples, which proves the effectiveness of characteristic wavelength combination. This result can provide some data and theoretical support for monitoring diseases and pests in citrus plantations.