ArticleScientific reports2024
YOLOv8-CML: a lightweight target detection method for color-changing melon ripening in intelligent agriculture.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- An Automated Population Monitoring Framework forSensors (Basel, Switzerland) · 2026Article
- AgroDualNet: a dual deep learning-based crop disease forecasting and fruit ripening detection.Scientific reports · 2026Article
- DAQ-YOLO:a high precision counting model for maize seedlings in dense scenes.Frontiers in plant science · 2026Article
- A faster and lighter weight robotic ready model YOLO Punica for detecting pomegranate fruit development.Scientific reports · 2025Article
- Small target detection in coal mine underground based on improved RTDETR algorithm.Scientific reports · 2025Article
- BiFPN-enhanced SwinDAT-based cherry variety classification with YOLOv8.Scientific reports · 2025Article
- Study of conveyor belt deviation detection based on improved YOLOv8 algorithm.Scientific reports · 2024Article
- Survivor detection approach for post earthquake search and rescue missions based on deep learning inspired algorithms.Scientific reports · 2024Article
- Iterative optimization annotation pipeline and ALSS-YOLO-Seg for efficient banana plantation segmentation in UAV imagery.Frontiers in plant science · 2024Article
- YOLO-SDL: a lightweight wheat grain detection technology based on an improved YOLOv8n model.Frontiers in plant science · 2024Article
- YOLOv8-segANDcal: segmentation, extraction, and calculation of soybean radicle features.Frontiers in plant science · 2024Article
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6 authors.
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Abstract
Color-changing melon is an ornamental and edible fruit. Aiming at the problems of slow detection speed and high deployment cost for Color-changing melon in intelligent agriculture equipment, this study proposes a lightweight detection model YOLOv8-CML.Firstly, a lightweight Faster-Block is introduced to reduce the number of memory accesses while reducing redundant computation, and a lighter C2f structure is obtained. Then, the lightweight C2f module fusing EMA module is constructed in Backbone to collect multi-scale spatial information more efficiently and reduce the interference of complex background on the recognition effect. Next, the idea of shared parameters is utilized to redesign the detection head to simplify the model further. Finally, the α-IoU loss function is adopted better to measure the overlap between the predicted and real frames using the α hyperparameter, improving the recognition accuracy. The experimental results show that compared to the YOLOv8n model, the parametric and computational ratios of the improved YOLOv8-CML model decreased by 42.9% and 51.8%, respectively. In addition, the model size is only 3.7 MB, and the inference speed is improved by 6.9%, while mAP@0.5, accuracy, and FPS are also improved. Our proposed model provides a vital reference for deploying Color-changing melon picking robots.
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