A Novel Approach to Grade Cotton Aphid Damage Severity with Hyperspectral Index Reconstruction

Abstract

As a kind of important insect pest of cotton crops, aphids cause serious damage in cotton yields and quality worldwide, posing a significant risk to economic losses. Automatic detection of the pest damage level plays an important role in cotton field management. However, it is usually regarded as a classification problem in machine learning, where the disease severity levels are taken as independent categories and the inter-level relationship has not fully been considered. To utilize the inherited relations among different severity levels caused by cotton aphids, a novel approach based on the spectral index reconstruction was proposed in this study. First, six types of initial spectral indices were reconstructed based on healthy samples in the training set. Then, the severity sequences corresponding to the reconstructed initial spectral indices (RISIs) were sorted and compared with the ideal sequence. After attaining sequences most consistent with the ideal one, the ratio between the inter- and intra- levels was calculated to select the sensitive RISI. Moreover, the range of each severity level was established by the thresholds between adjacent grades of the selected sensitive RISI, which was finally used to determine the disease severity level caused by cotton aphids. Results of the cotton aphids showed that the proposed approach achieved a grading performance with OA = 0.944, AA = 0.900, and Kappa coefficient = 0.928. Hence, the proposed approach based on hyperspectral index reconstruction is effective and has potential application in grading the aphid infestation severity of cotton.