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ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Technical Advances in Plant Science
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1541365
This article is part of the Research Topic Machine Vision and Machine Learning for Plant Phenotyping and Precision Agriculture, Volume II View all 13 articles
Full-dimensional dynamic convolution and progressive learning strategy for strawberry recognition based on YOLOv8
Provisionally accepted- Macau Institute of Systems Engineering, Macau University of Science and Technology, Taipa, China
The growth of strawberries is characterized by environmental diversity and spatial dispersion, which presents challenges such as accurate identification and real-time image processing in complex environments.To address these challenges, we developed an advanced recognition model based on YOLOv8. We replaced the traditional backbone structure with an EfficientNetV2 feature extraction network and utilized ODConv instead of standard convolution. For the loss function, we implemented a dynamic non-monotonic focusing mechanism and introduced Wise-IoU to replace the traditional CIoU. Compared to the original YOLOv8 model, the proposed model shows significant improvements in mAP50, precision, and recall rate, with increases of 16.91%, 14.92%, and 8.4%, respectively. Furthermore, the lightness of this model is increased by 15.67%.According to the experimental results, the proposed model can identify strawberries of different ripeness more accurately than the original model. At the same time, the model is lightweight and easy to deploy on the picking robot.
Keywords: Strawberries Recognition, target detection, Improved YOLOv8, EfficientNetv2, ODConv, Wise-IoU
Received: 07 Dec 2024; Accepted: 04 Mar 2025.
Copyright: © 2025 Bai, Xia, Liu, Yang and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Tai Zhang, Macau Institute of Systems Engineering, Macau University of Science and Technology, Taipa, China
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