Tomato is one of the most popular and widely cultivated fruits and vegetables in the world. In large-scale cultivation, manual picking is inefficient and labor-intensive, which is likely to lead to a decline in the quality of the fruits.
Although mechanical picking can improve efficiency, it is affected by factors such as leaf occlusion and changes in light conditions in the tomato growth environment, resulting in poor detection and recognition results. To address these challenges, this study proposes a tomato detection method based on Graph-CenterNet. The method employs Vision Graph Convolution (ViG) to replace traditional convolutions, thereby enhancing the flexibility of feature extraction, while reducing one downsampling layer to strengthen global information capture. Furthermore, the Coordinate Attention (CA) module is introduced to optimize the processing of key information through correlation computation and weight allocation mechanisms. Experiments conducted on the Tomato Detection dataset demonstrate that the proposed method achieves average precision improvements of 7.94%, 10.58%, and 1.24% compared to Faster R-CNN, CenterNet, and YOLOv8, respectively.
The results indicate that the improved Graph-CenterNet method significantly enhances the accuracy and robustness of tomato detection in complex environments.
Long, C.-F.; Yang, Y.-J.; Liu, H.-M.; Su, F.; Deng, Y.-J. An Approach for Detecting Tomato Under a Complicated Environment. Agronomy 2025, 15, 667. https://doi.org/10.3390/agronomy15030667
Source: MDPI