In order to solve the problems that existing tomato maturity detection methods struggle to take into account both common tomato and cherry tomato varieties in complex field environments (such as light change, occlusion, and fruit overlap) and the model size being too large, this paper proposes a lightweight tomato maturity detection model based on improved YOLO11, named GFS-YOLO11.
To achieve a lightweight network, we propose the C3k2_Ghost module to replace the C3K2 module in the original network, which can ensure a feature extraction capability and reduce model computation. To compensate for the potential feature loss caused by the lightweight, this paper proposes a feature-refining module (FRM). After embedding each feature extraction module in the trunk network, it improves the feature expression ability of common tomatoes and cherry tomatoes in complex field environments using depth-separable convolution, multi-scale pooling, channel attention, and spatial attention mechanisms. In addition, to further improve the detection ability of the model for tomatoes of different sizes, the SPPFELAN module is also proposed in this paper. In combining the advantages of SPPF and ELAN, multiple parallel SPPF branches are used to extract features of different levels and perform splicing and fusion. To verify the validity of the method, this study constructed a dataset of 1061 images of common and cherry tomatoes, covering tomatoes in six ripened categories.
The experimental results show that the performance of the GFS-YOLO11 model is significantly improved compared with the original model; the P, R, mAP50, and MAP50-95 increased by 5.8%, 4.9%, 6.2%, and 5.5%, respectively, and the number of parameters and calculation amount was reduced by 35.9% and 22.5%, respectively. The GFS-YOLO11 model is lightweight while maintaining high precision, can effectively cope with complex field environments, and more conveniently meets the needs of real-time maturity detection of common tomatoes and cherry tomatoes.
Wei, J.; Ni, L.; Luo, L.; Chen, M.; You, M.; Sun, Y.; Hu, T. GFS-YOLO11: A Maturity Detection Model for Multi-Variety Tomato. Agronomy 2024, 14, 2644. https://doi.org/10.3390/agronomy14112644
Source: MDPI