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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.

Color-changing melons are a kind of cucurbit plant that combines ornamental and food. With the aim of increasing the efficiency of harvesting Color-changing melon fruits while reducing the deployment cost of detection models on agricultural equipment, this study presents an improved YOLOv8s network approach that uses model pruning and knowledge distillation techniques. The method first merges Dilated Wise Residual (DWR) and Dilated Reparam Block (DRB) to reconstruct the C2f module in the Backbone for better feature fusion. Next, we designed a multilevel scale fusion feature pyramid network (HS-PAN) to enrich semantic information and strengthen localization information to enhance the detection of Color-changing melon fruits with different maturity levels. Finally, we used Layer-Adaptive Sparsity Pruning and Block-Correlation Knowledge Distillation to simplify the model and recover its accuracy. In the Color-changing melon images dataset, the mAP0.5 of the improved model reaches 96.1%, the detection speed is 9.1% faster than YOLOv8s, the number of Params is reduced from 6.47M to 1.14M, the number of computed FLOPs is reduced from 22.8GFLOPs to 7.5GFLOPs. The model’s size has also decreased from 12.64MB to 2.47MB, and the performance of the improved YOLOv8 is significantly more outstanding than other lightweight networks. The experimental results verify the effectiveness of the proposed method in complex scenarios, which provides a reference basis and technical support for the subsequent automatic picking of Color-changing melons.