Abstract
Plant disease detection technology is an important part of the intelligent agricultural Internet of Things monitoring system. The real natural environment requires the plant disease detection system to have extremely high real time detection and accuracy. The lightweight network MobileNetv2-YOLOv3 model can meet the real-time detection, but the accuracy is not enough to meet the actual needs. This study proposed a multiscale parallel algorithm MP-YOLOv3 based on the MobileNetv2-YOLOv3 model. The proposed method put forward a multiscale feature fusion method, and an efficient channel attention mechanism was introduced into the detection layer of the network to achieve feature enhancement. The parallel detection algorithm was used to effectively improve the detection performance of multiscale tomato gray mold lesions while ensuring the real-time performance of the algorithm. The experimental results show that the proposed algorithm can accurately and real-time detect multiscale tomato gray mold lesions in a real natural environment. The F1 score and the average precision reached 95.6 and 93.4% on the self-built tomato gray mold detection dataset. The model size was only 16.9 MB, and the detection time of each image was 0.022 s.
Highlights
Plant diseases are the main cause of food loss in the world’s economy
The image resolution is adjusted to 416 × 416 and it is inputted into the MobileNetv2 network to extract features, and the fused feature map is obtained by multiscale feature fusion
(1) The method proposed in this study can identify the tomato gray mold object from images with complex background, and it is expected to be applied in tomato growth information monitoring and tomato disease automated inspection
Summary
Plant diseases are the main cause of food loss in the world’s economy. Food loss from crop infections caused by pathogens such as bacteria, viruses, and fungi is a persistent problem. This situation is further complicated by the fact that disease is more likely to metastasize globally than ever before. In order to minimize the damage caused by diseases during crop growth, crop prevention is imperative. Crop inspections and plant diseases are determined by farmers or experts with some training or experience. This manual method is expensive because it requires continuous monitoring and is not feasible for larger areas
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