ArticleInsects2023
Detection of Rice Pests Based on Self-Attention Mechanism and Multi-Scale Feature Fusion.
Article in Insects, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Synergistic effect of composite microbial bioactive metabolites in the control of rice diseases and pest insects under reduced-dosage chemical pesticide application.Frontiers in microbiology · 2026Article
- HDA-YOLO: a hierarchical and densely-fused attention network for rice pest detection in complex agricultural environments.Frontiers in plant science · 2026Article
- Towards smart farming: a real-time diagnosis system for strawberry foliar diseases using deep learning.BMC plant biology · 2025Article
- Comparative analysis of attentional mechanisms in rice pest identification.Scientific reports · 2025Article
- Rice Canopy Disease and Pest Identification Based on Improved YOLOv5 and UAV Images.Sensors (Basel, Switzerland) · 2025Article
- Apnet: Lightweight network for apricot tree disease and pest detection in real-world complex backgrounds.Plant methods · 2025Article
- LDL-MobileNetV3S: an enhanced lightweight MobileNetV3-small model for potato leaf disease diagnosis through multi-module fusion.Frontiers in plant science · 2025Article
- P4CN-YOLOv5s: a passion fruit pests detection method based on lightweight-improved YOLOv5s.Frontiers in plant science · 2025Article
- GhostConv+CA-YOLOv8n: a lightweight network for rice pest detection based on the aggregation of low-level features in real-world complex backgrounds.Frontiers in plant science · 2025Article
- SM-CycleGAN: crop image data enhancement method based on self-attention mechanism CycleGAN.Scientific reports · 2024Article
- A high-precision jujube disease spot detection based on SSD during the sorting process.PloS one · 2024Article
- Deep learning-based rice pest detection research.PloS one · 2024Article
- Improvement of the YOLOv5 Model in the Optimization of the Brown Spot Disease Recognition Algorithm of Kidney Bean.Plants (Basel, Switzerland) · 2023Article
- A Novel Deep Learning Model for Accurate Pest Detection and Edge Computing Deployment.Insects · 2023Article
- Article
- TeaDiseaseNet: multi-scale self-attentive tea disease detection.Frontiers in plant science · 2023Article
Corrections and comments
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Authors and funding
6 authors.
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Abstract
In recent years, the occurrence of rice pests has been increasing, which has greatly affected the yield of rice in many parts of the world. The prevention and cure of rice pests is urgent. Aiming at the problems of the small appearance difference and large size change of various pests, a deep neural network named YOLO-GBS is proposed in this paper for detecting and classifying pests from digital images. Based on YOLOv5s, one more detection head is added to expand the detection scale range, the global context (GC) attention mechanism is integrated to find targets in complex backgrounds, PANet is replaced by BiFPN network to improve the feature fusion effect, and Swin Transformer is introduced to take full advantage of the self-attention mechanism of global contextual information. Results from experiments on our insect dataset containing Crambidae, Noctuidae, Ephydridae, and Delphacidae showed that the average mAP of the proposed model is up to 79.8%, which is 5.4% higher than that of YOLOv5s, and the detection effect of various complex scenes is significantly improved. In addition, the paper analyzes and discusses the generalization ability of YOLO-GBS model on a larger-scale pest data set. This research provides a more accurate and efficient intelligent detection method for rice pests and others crop pests.
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