Affiliation:
1. College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China
2. Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Hangzhou 311300, China
3. China Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Hangzhou 311300, China
4. College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China
Abstract
Accurately detecting and identifying granary pests is important in effectively controlling damage to a granary, ensuring food security scientifically and efficiently. In this paper, multi-scale images of seven common granary pests were collected. The dataset had 5231 images acquired with DSLR-shot, microscope, cell phone and online crawler. Each image contains different species of granary pests in a different background. In this paper, we designed a multi-scale granary pest recognition model, using the YOLOv5 (You Look Only Once version 5) object detection algorithm incorporating bidirectional feature pyramid network (BiFPN) with distance intersection over union, non-maximum suppression (DIOU_NMS) and efficient channel attention (ECA) modules. In addition, we compared the performance of the different models established with Efficientdet, Faster rcnn, Retinanet, SSD, YOLOx, YOLOv3, YOLOv4 and YOLOv5s, and we designed improved YOLOv5s on this dataset. The results show that the average accuracy of the model we designed for seven common pests reached 98.2%, which is the most accurate model among those identified in this paper. For further detecting the robustness of the proposed model, ablation analysis was conducted. Furthermore, the results show that the average accuracy of models established using the YOLOv5s network model combined with the attention mechanism was 96.9%. When replacing the model of PANet with BiFPN, the average accuracy reached 97.2%. At the same time, feature visualization was analyzed. The results show that the proposed model is good for capturing features of pests. The results of the model have good practical significance for the recognition of multi-scale granary pests.
Funder
Key R&D Projects in Zhejiang Province
Basic Public Welfare Project of Zhejiang Province
Zhejiang Province, Agriculture, agriculture and nine aspects of science and technology cooperation project
Subject
Plant Science,Agronomy and Crop Science,Food Science
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