MCD-Yolov5: Accurate, Real-Time Crop Disease and Pest Identification Approach Using UAVs

Abstract

As the principal factor affecting global food production, accurate identification of agricultural pests and diseases is crucial in ensuring a sustainable food supply. However, existing methods lack sufficient performance in terms of accuracy and real-time detection of multiple pests and diseases. Accordingly, accurate, efficient, and real-time identification of a wide range of pests and diseases is challenging. To address this, we propose an MCD-Yolov5 with a fusion design that combines multi-layer feature fusion (MLFF), convolutional block attention module CBAM, and detection transformer (DETF). In this model, we optimize the MLFF design to realize the dynamic adjustment of feature weights of the input feature layer to (1) find an appropriate distribution of feature information proportion for the detection task, (2) enhance detection speed by efficiently extracting effective images and effective features through CBAM, and (3) improve feature extraction capability through DETF to compensate for the accuracy problem of multiple pest detection. In addition, we established an unmanned aerial vehicle system (UAV) for crop pest and disease detection to assist in detection and prevention. We validate the performance of the proposed method through an established UAV platform, and five indicators are employed to quantify the performance. MCD-Yolov5 can detect pests and diseases with a large improvement in detection accuracy and detection efficiency, obtaining an 88.12% accuracy. The proposed method and system provide an idea for the effective identification of pests and diseases.