Investigation of Peanut Leaf Spot Detection Using Superpixel Unmixing Technology for Hyperspectral UAV Images

Leaf spot disease significantly impacts peanut growth. Timely, effective, and accurate monitoring of leaf spot severity is crucial for high-yield and high-quality peanut production. Hyperspectral technology from unmanned aerial vehicles (UAVs) is widely employed for disease detection in agricultural fields, but the low spatial resolution of imagery affects accuracy. In this study, peanuts with varying levels of leaf spot disease were detected using hyperspectral images from UAVs. Spectral features of crops and backgrounds were extracted using simple linear iterative clustering (SLIC), the homogeneity index, and k-means clustering. Abundance estimation was conducted using fully constrained least squares based on a distance strategy (D-FCLS), and crop regions were extracted through threshold segmentation. Disease severity was determined based on the average spectral reflectance of crop regions, utilizing classifiers such as XGBoost, the MLP, and the GA-SVM. Results indicate that crop spectra extracted using the superpixel-based unmixing method effectively captured spectral variability, leading to more accurate disease detection. By optimizing threshold values, a better balance between completeness and the internal variability of crop regions was achieved, allowing for the precise extraction of crop regions. Compared to other unmixing methods and manual visual interpretation techniques, the proposed method achieved excellent results, with an overall accuracy of 89.08% and a Kappa coefficient of 85.42% for the GA-SVM classifier. This method provides an objective, efficient, and accurate solution for detecting peanut leaf spot disease, offering technical support for field management with promising practical applications.