A novel vegetation index for monitoring the stress levels of pest caused by dusky cotton bug

Abstract

The research introduced a novel vegetation index (DCBVI) to effectively monitor dusky cotton bug infestations, addressing the limitations of existing indices, which show low sensitivity to these infestations. Non-imaging hyperspectral data and UAV multispectral data of the cotton canopy were collected during the flowering and boll development stages over two years, and the first-order differential values of each hyperspectral band were calculated. The Pearson correlation coefficient and Relief-F algorithm were used to select the hyperspectral bands associated with insect stress caused by the dusky cotton bug. Four novel vegetation indices were constructed based on difference, ratio, normalization and chlorophyll vegetation index. The vegetation index most strongly correlated with insect pest stress was selected and designated as the Dusky Cotton Bug Vegetation Index (DCBVI). Combined with the existing vegetation indices, GDLR, SVM and CatBoost were used to construct the pest stress classification model of dusky cotton bug, and DCBVI was derived using relevant bands from UAV multispectral remote sensing data. The pest discrimination model for the cotton bug was established, and the pest distribution in cotton fields was visualized. To research the effect of dusky cotton bug infestation on cotton growth, SPAD inversion model for different infestation levels were established by KNN, RF and CatBoost. The Firefly algorithm (FA), Bat algorithm (BA), and Genetic algorithm (GA) were used to optimize both the classification and discrimination models for dusky cotton bug infestation and the cotton SPAD inversion model. The results showed that the correlation between DCBVI and the pest stress level of the dusky cotton bug was 0.833, and the correlation between DCBVI and SPAD was higher than that of existing vegetation indices. The accuracy, precision, recall, and F1 score of the CatBoost dusky cotton bug insect pest classification model, optimized by FA based on DCBVI and the existing vegetation index, reached 93.1%, 93.2%, 92.9%, and 92.8%, respectively. When the BA-CatBoost model was used to invert the SPAD values of the dusky cotton bug under different insect stress, the R² and RMSE of the optimal model were 0.949 and 1.619. This research significantly improves the accuracy of dusky cotton bug pest monitoring and provides a method for constructing a novel sensitivity index based on hyperspectral data for monitoring other insect pests.