ArticleSensors (Basel, Switzerland)2025
YOLO-ALW: An Enhanced High-Precision Model for Chili Maturity Detection.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- MSRRT-DETR: A high-precision apple detection method with strong cross-domain generalization capability in complex orchard scenes.PloS one · 2026Article
- HAV-Pose: synergizing hybrid attention networks and spherical visibility fields for obstacle-aware fruit harvesting.Frontiers in plant science · 2026Article
- YOLO-TPS: A Multi-Module Synergistic High-Precision Fish-Disease Detection Model for Complex Aquaculture Environments.Animals : an open access journal from MDPI · 2025Article
- YOLO-RGDD: A Novel Method for the Online Detection of Tomato Surface Defects.Foods (Basel, Switzerland) · 2025Article
- A bibliometric review of deep learning in crop monitoring: trends, challenges, and future perspectives.Frontiers in artificial intelligence · 2025Review
- Application of real-time detection transformer based on convolutional block attention module and grouped convolution in maize seedling.Frontiers in plant science · 2025Article
- A review of visual perception technology for intelligent fruit harvesting robots.Frontiers in plant science · 2025Review
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8 authors.
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Abstract
Chili pepper, a widely cultivated and consumed crop, faces challenges in accurately determining maturity due to issues such as occlusion, small target size, and similarity between fruit color and background. This study presents an enhanced YOLOv8n-based object detection model, YOLO-ALW, designed to address these challenges. The model introduces the AKConv (Alterable Kernel Convolution) module in the head section, which adaptively adjusts the convolution kernel shape and size based on the target and scene, improving detection performance under occlusion and dense environments. In the backbone, the SPPF_LSKA (Spatial Pyramid Pooling Fast-Large Separable Kernel Attention) module enhances the integration of multi-scale features, facilitating accurate differentiation of peppers at various maturity stages while maintaining low computational complexity. Additionally, the Wise-IoU (Wise Intersection over Union) loss function optimizes bounding box learning, further improving the detection of peppers in occluded or background-similar scenarios. Experimental results demonstrate that YOLO-ALW achieves a mean average precision (mAP
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