ArticleSensors (Basel, Switzerland)2023
Yolo-Pest: An Insect Pest Object Detection Algorithm via CAC3 Module.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Fine-Grained Recognition of Insect Pests from Digital Images: A Survey.Neotropical entomology · 2026Pooled it
- Article
- GrainPest-SSL: A Lightweight Semi-Supervised Detector for Stored-Grain Pest Monitoring in Smart Granaries.Sensors (Basel, Switzerland) · 2026Article
- Evaluation of YOLOv7-v13 models for multi-class small insect pest detection using the five-pest dataset.Scientific reports · 2026Article
- EP-YOLO: An Enhanced Lightweight Model for Micro-Pest Detection in Agricultural Light-Trap Environments.Sensors (Basel, Switzerland) · 2026Article
- Attention-PestNet: hierarchical scaled dot-product attention for insect pest detection.Plant methods · 2025Article
- Real time sugarcane stem health detection with YOLOv8 tinier and RepNCSP ELAN.Scientific reports · 2025Article
- YOLOv8 forestry pest recognition based on improved re-parametric convolution.Frontiers in plant science · 2025Article
- A counting method of whiteflies on crop leave images captured by AR glasses based on segmentation and improved YOLOv11 models.Frontiers in plant science · 2025Article
- A lightweight rice pest detection algorithm based on improved YOLOv8.Scientific reports · 2024Article
- Efficient Optimized YOLOv8 Model with Extended Vision.Sensors (Basel, Switzerland) · 2024Article
- From beasts to bytes: Revolutionizing zoological research with artificial intelligence.Zoological research · 2023Review
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6 authors.
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Abstract
Insect pests have always been one of the main hazards affecting crop yield and quality in traditional agriculture. An accurate and timely pest detection algorithm is essential for effective pest control; however, the existing approach suffers from a sharp performance drop when it comes to the pest detection task due to the lack of learning samples and models for small pest detection. In this paper, we explore and study the improvement methods of convolutional neural network (CNN) models on the Teddy Cup pest dataset and further propose a lightweight and effective agricultural pest detection method for small target pests, named Yolo-Pest, for the pest detection task in agriculture. Specifically, we tackle the problem of feature extraction in small sample learning with the proposed CAC3 module, which is built in a stacking residual structure based on the standard BottleNeck module. By applying a ConvNext module based on the vision transformer (ViT), the proposed method achieves effective feature extraction while keeping a lightweight network. Comparative experiments prove the effectiveness of our approach. Our proposal achieves 91.9% mAP0.5 on the Teddy Cup pest dataset, which outperforms the Yolov5s model by nearly 8% in mAP0.5. It also achieves great performance on public datasets, such as IP102, with a great reduction in the number of parameters.
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