Identification and Monitoring of Crop Pests and Diseases Based on Remote Sensing Technology

Abstract

Identifying and monitoring crop pests and diseases are crucial to agricultural production, and they directly affect crop growth and the quality of agricultural products. This paper reviews crop pest and disease identification and monitoring techniques based on remote sensing imagery, emphasizing their advantages in providing timely and accurate information. The paper mainly summarizes the data sources for crop pest and disease detection, including satellite, UAV, aircraft, ground, and aerial remote sensing data, as well as laboratory monitoring data and seven types of multi-source data fusion. Four remote sensing monitoring techniques were discussed: spectral reflectance analysis, vegetation index analysis, regression model analysis, and spectral differential analysis, and their effects in practical applications were demonstrated through case studies. These methods significantly improve the accuracy and efficiency of pest and disease monitoring. The spectral reflectance analysis method directly reflects the changes in spectral characteristics of crops, and the vegetation index analysis method improves the indicative nature of monitoring by integrating the spectral characteristics of vegetation. The regression model analysis method, on the other hand, provides quantitative estimation of the extent of pests and diseases through mathematical modelling. Spectral differential analysis reveals subtle changes in the spectral profile of the crop and helps in the early identification of pests and diseases.