CTDA: an accurate and efficient cherry tomato detection algorithm in complex environments

文献类型： 外文期刊

第一作者： Liang, Zhi

作者： Liang, Zhi;Lin, Zhonglong;Li, Xiaojuan;Zou, Xiangjun;Zhang, Caihong;Wang, Guoqiang

作者机构：

关键词： picking robot; cherry tomato detection; deep learning; YOLO; multi-scale feature fusion

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：4.8； 五年影响因子：5.7 ）

ISSN： 1664-462X

年卷期： 2025 年 16 卷

页码：

收录情况： SCI

摘要： Introduction In the natural harvesting conditions of cherry tomatoes, the robotic vision for harvesting faces challenges such as lighting, overlapping, and occlusion among various environmental factors. To ensure accuracy and efficiency in detecting cherry tomatoes in complex environments, the study proposes a precise, realtime, and robust target detection algorithm: the CTDA model, to support robotic harvesting operations in unstructured environments.Methods The model, based on YOLOv8, introduces a lightweight downsampling method to restructure the backbone network, incorporating adaptive weights and receptive field spatial characteristics to ensure that low-dimensional small target features are not completely lost. By using softpool to replace maxpool in SPPF, a new SPPFS is constructed, achieving efficient feature utilization and richer multi-scale feature fusion. Additionally, by incorporating a dynamic head driven by the attention mechanism, the recognition precision of cherry tomatoes in complex scenarios is enhanced through more effective feature capture across different scales.Results CTDA demonstrates good adaptability and robustness in complex scenarios. Its detection accuracy reaches 94.3%, with recall and average precision of 91.5% and 95.3%, respectively, while achieving a mAP@0.5:0.95 of 76.5% and an FPS of 154.1 frames per second. Compared to YOLOv8, it improves mAP by 2.9% while maintaining detection speed, with a model size of 6.7M.Discussion Experimental results validate the effectiveness of the CTDA model in cherry tomato detection under complex environments. While improving detection accuracy, the model also enhances adaptability to lighting variations, occlusion, and dense small target scenarios, and can be deployed on edge devices for rapid detection, providing strong support for automated cherry tomato picking.

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