ArticleInsects2023
Maize-YOLO: A New High-Precision and Real-Time Method for Maize Pest Detection.
Article in Insects, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- Comparative Evaluation of CNN and Transformer-Based Instance Segmentation Architectures for Whitefly Detection in Citrus Crops.Plants (Basel, Switzerland) · 2026Article
- AI-IoT driven system for agricultural pest outbreak risk prediction.Scientific reports · 2026Article
- DELP-YOLOv12: a lightweight deployable model for maize pest and disease detection.Plant methods · 2026Article
- PestRefineDet: integrating DenseNet with RefineDet for precision pest detection in agriculture.Frontiers in plant science · 2026Article
- Focal-HAIN: a lightweight model with adaptive modulation and hierarchical interaction for real-time crop pest and disease monitoring.Frontiers in plant science · 2026Article
- WSD-YOLO: A lightweight YOLO-based model with enhanced feature representation for maize pest detection.PloS one · 2026Article
- Pest Detection in Edible Crops at the Edge: An Implementation-Focused Review of Vision, Spectroscopy, and Sensors.Sensors (Basel, Switzerland) · 2025Review
- YOLO-PEST: a novel rice pest detection approach based on YOLOv5s.Plant methods · 2025Article
- YOLOv8-DBW: An Improved YOLOv8-Based Algorithm for Maize Leaf Diseases and Pests Detection.Sensors (Basel, Switzerland) · 2025Article
- RDW-YOLO: A Deep Learning Framework for Scalable Agricultural Pest Monitoring and Control.Insects · 2025Article
- Rethinking feature representation and attention mechanisms in intelligent recognition of leaf pests and diseases in wheat.Scientific reports · 2025Article
- Yolo-pest: an optimized YoloV8x for detection of small insect pests using smart traps.Scientific reports · 2025Article
- Hyperparameter Optimization for Tomato Leaf Disease Recognition Based on YOLOv11m.Plants (Basel, Switzerland) · 2025Article
- YOLOv8 forestry pest recognition based on improved re-parametric convolution.Frontiers in plant science · 2025Article
- YOLOv8s-CGF: a lightweight model for wheat ear Fusarium head blight detection.PeerJ. Computer science · 2024Article
- TP-Transfiner: high-quality segmentation network for tea pest.Frontiers in plant science · 2024Article
- A Novel Deep Learning Model for Accurate Pest Detection and Edge Computing Deployment.Insects · 2023Article
- Classification and localization of maize leaf spot disease based on weakly supervised learning.Frontiers in plant science · 2023Article
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9 authors.
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Abstract
The frequent occurrence of crop pests and diseases is one of the important factors leading to the reduction of crop quality and yield. Since pests are characterized by high similarity and fast movement, this poses a challenge for artificial intelligence techniques to identify pests in a timely and accurate manner. Therefore, we propose a new high-precision and real-time method for maize pest detection, Maize-YOLO. The network is based on YOLOv7 with the insertion of the CSPResNeXt-50 module and VoVGSCSP module. It can improve network detection accuracy and detection speed while reducing the computational effort of the model. We evaluated the performance of Maize-YOLO in a typical large-scale pest dataset IP102. We trained and tested against those pest species that are more damaging to maize, including 4533 images and 13 classes. The experimental results show that our method outperforms the current state-of-the-art YOLO family of object detection algorithms and achieves suitable performance at 76.3% mAP and 77.3% recall. The method can provide accurate and real-time pest detection and identification for maize crops, enabling highly accurate end-to-end pest detection.
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