A Recognition Method for Adzuki Bean Rust Disease Based on Spectral Processing and Deep Learning Model

Adzuki bean rust disease is an important factor restricting the yield of the adzuki bean. Late prevention and control at the early stage of the disease will lead to crop failure. Traditional diagnosis methods of adzuki bean rust disease mainly rely on field observations and laboratory tests, which are inefficient, time-consuming, highly dependent on professional knowledge, and cannot meet the requirements of modern agriculture for rapid and accurate diagnosis. To address this issue, a diagnosis method of adzuki bean rust disease was proposed using spectroscopy and deep learning methods. First, visible/near-infrared (UV/VNIR) spectroscopy was used to extract the spectral information of leaves, and discrete wavelet transform (DWT) was applied to preprocess and smooth the original canopy spectral data to effectively reduce the impact of noise interference. Second, the competitive adaptive reweighted sampling (CARS) algorithm was implemented in the range of 425–825 nm to determine the optimal characteristic wavenumbers, thereby reducing data redundancy. Finally, 51 characteristic wavenumbers were selected and imported into the LeNet-5 deep learning model for simulation and evaluation. The results showed that the accuracy, precision, recall, and F1 score on the test set were 99.65%, 98.04%, 99.01%, and 98.52%, respectively. The proposed DWT-CARS-LeNet-5 model can diagnose adzuki bean rust quickly, accurately, and non-destructively. This method can provide a cutting-edge solution for improving the accuracy of prevention and control of adzuki bean rust disease in agricultural practice.