Cotton pest monitoring based on Logistic algorithm and remote sensing image

【Objective】 The purpose of this article is to monitor cotton pest in field based on Logistic algorithms and multi-spectral remote sensing images.【Method】 The cotton areas with insect pests were selected as the research object. The multi-spectral remote sensing images of cotton field were acquired by UAV, and then pre-processed. Based on the spectral characteristics of cotton pests, the Logistic regression model was constructed by the reflectivity of pest-sensitive band and vegetation index to identify and monitor cotton pests.【Result】 The cotton aphid, cotton red spider mite, and cotton bollworm identification models constructed by the soil adjusted vegetation index (SAVI) model and the normalized vegetation index (NDVI) model were the optimal models, and their accuracy for training sample and test sample reached 93.7% and 90.5% respectively the recall rate and F1 value were 96.6% and 93.5% respectively and the determination coeffecients of recognition models for three types of pests were 0.942, 0.851 and 0.663 respectively. 【Conclusion】 This model can identify the occurrence area of cotton aphid, cotton red spider mite and cotton bollworm, which can basically meet the requirements of precision plant protection operation in cotton field.