Detection of fungal disease in citrus fruit based on hyperspectral imaging

Citrus fruit fungal disease is a major reason for the serious decline in citrus production and quality. Due to its highly contagious nature, timely and effective detection is an important means of prevention and control. Given the high similarity between citrus quarantine diseases and local similar diseases after invading citrus fruits, this study utilizes hyperspectral imaging technology to acquire hyperspectral images of citrus diseases caused by three types of fungi ( Phytophthora citrophthora , Phytophthora citricola , Phytophthora syringae ). By studying the spectral features of different regions affected by citrus diseases, the competitive adaptive resampling algorithm (CARS) was used to extract 44 feature bands for reconstructing the spectral image, aiming to reduce information redundancy without losing critical information. A simple deep learning model architecture was proposed, which achieved an accuracy of 92.50% in the test dataset. This study provides a new perspective and method for citrus disease detection, offering theoretical and scientific support for the detection of citrus diseases using deep learning and hyperspectral imaging technology.