Latest Research in Computer Vision and Pattern Recognition

Top Research Topics in Computer Vision and Pattern Recognition

• Smart Agriculture and AI — 29 papers
• Computer Vision and Pattern Recognition — 5 papers
• Advanced Neural Network Applications — 2 papers
• Date Palm Research Studies — 1 papers
• Neurobiology and Insect Physiology Research — 1 papers
• Silk-based biomaterials and applications — 1 papers
• Remote Sensing in Agriculture — 1 papers
• Allergic Rhinitis and Sensitization — 1 papers
• Spectroscopy and Chemometric Analyses — 1 papers
• Animal Vocal Communication and Behavior — 1 papers

Highest-Cited Papers

1. SPROUT: A Scalable Diffusion Foundation Model for Agricultural Vision
2. A Deep Learning-Driven Computer Vision and Web-Based Monitoring System for Automated Nutrient Management in Soilless Agriculture
3. Perturbation-Aware Diffusion-Guided Hybrid Segmentation for Robust and Annotation-Efficient Plant Stress Phenotyping
4. BDL-Net: A blended deep learning approach for pest detection in agriculture using IoT-enabled sound analysis
5. Multi-modal fusion of acoustic and microwave signals for early detection of Red Palm Weevil infestation
6. Integrating machine learning, deep learning, and image analysis for seed species classification
7. Soybean Field Weed Segmentation and Prescription Map Generation Based on SCG-UNet Fusion of UAV RGB and Multispectral Images
8. CGMap: A Geospatially Aware Deep Learning Framework for Crop Gap Mapping Using UAV
9. Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data
10. Improving Out-of-Distribution Robustness for Wheat Head Detection: A Lightweight Modified YOLOv13 Approach
11. Model evaluation for automated scoring of electropenetrography waveform data from mosquitoes
12. Light-FCCNet: A Compact Multi-Scale Framework for Accurate Field Crop Counting
13. A YOLO11-MSC-Based Method for Small Pest Detection in Insect Pest Monitoring Lamp Images
14. HFU-YOLO: A Method for Detecting Underground Pests in Complex Soil Backgrounds
15. An End-to-End Precision Phenotyping Framework: Rice Panicle Detection and Counting in Complex Fields via Lightweight DETR
16. A method for recognizing and detecting the physiological state of silkworms based on an improved YOLOv8 approach
17. Explainable attention-based deep learning framework for automated cotton weed classification
18. Automatic preprocessing pipeline for individual-plant level (IPL) soybean growth monitoring through UAV multisource imagery
19. Image Databases for Pollen Grain Classification: Availability, Quality, and Challenges
20. MBP-RTDETR: A Lightweight and Efficient Weed Detection Model for Field Crop Environments