Rapid and Nondestructive Evaluation of Rice SPAD Value under Disease Stress Using Hyperspectral Imaging Sensors

Leaf chlorophyll content is an important indicator of photosynthetic capacity and health status and plays a key role in monitoring plant growth. At present, research on chlorophyll content monitoring has mainly focused on establishing a relationship between vegetation index and chlorophyll content, which was composed of several hyperspectral wavelengths, resulting in the loss of effective information contained in certain wavelengths strongly associated with disease. Therefore, it was very important to select spectral wavelengths to reveal the hyperspectral response of chlorophyll content in diseased leaves. Fifty groups of rice leaves infected with bacterial leaf blight were studied, and hyperspectral images and soil plant analysis development (SPAD) values of disease‐stressed rice leaves were collected every 5 days from the first day after inoculation. A method for predicting the SPAD value of diseased rice leaves using random forest (RF) combined with a back‐propagation optimized neural network (BPNN) was proposed. The bird swarm algorithm (BSA) and the immune algorithm (IA) were used to solve the problems of information loss, local minimization, and slow convergence of the BPNN. The BPNN, BSA–BPNN, IA–BPNN, and BSA–IA–BPNN prediction models for the SPAD value of diseased rice leaves are established. The results show that the BSA–IA–BPNN model with characteristic wavelengths selected by RF achieved the best prediction effect, and the prediction sets R2, root‐mean‐square error, and relative error are 0.97%, 4.34%, and 3.33% respectively. The shortest prediction modeling time is 1.54 s, and the model exhibits the best stability. This study provides allegorical support and a theoretical basis for achieving rapid, nondestructive detection of the SPAD value of diseased rice leaves, provides an effective reference for estimating the SPAD value of diseased rice leaves in the field, and is of great importance for rice disease monitoring.