In the field of facility agriculture, the accurate identification of tomatoes at multiple stages has become a significant area of research. However, accurately identifying and localizing tomatoes in complex environments is a formidable challenge. Complex working conditions can impair the performance of conventional detection techniques, underscoring the necessity for more robust methods.
To address this issue, we propose a novel model of YOLOv8-EA for the localization and identification of tomato fruit. The model incorporates a number of significant enhancements. Firstly, the EfficientViT network replaces the original YOLOv8 backbone network, which has the effect of reducing the number of model parameters and improving the capability of the network to extract features. Secondly, some of the convolutions were integrated into the C2f module to create the C2f-Faster module, which facilitates the inference process of the model. Third, the bounding box loss function was modified to SIoU, thereby accelerating model convergence and enhancing detection accuracy. Lastly, the Auxiliary Detection Head (Aux-Head) module was incorporated to augment the network's learning capacity.
The accuracy, recall, and average precision of the YOLOv8-EA model on the self-constructed dataset were 91.4%, 88.7%, and 93.9%, respectively, with a detection speed of 163.33 frames/s. In comparison to the baseline YOLOv8n network, the model weight was increased by 2.07 MB, and the accuracy, recall, and average precision were enhanced by 10.9, 11.7, and 7.2 percentage points, respectively. The accuracy, recall, and average precision increased by 10.9, 11.7, and 7.2 percentage points, respectively, while the detection speed increased by 42.1%. The detection precision for unripe, semi-ripe, and ripe tomatoes was 97.1%, 91%, and 93.7%, respectively. On the public dataset, the accuracy, recall, and average precision of YOLOv8-EA are 91%, 89.2%, and 95.1%, respectively, and the detection speed is 1.8 ms, which is 4, 4.21, and 3.9 percentage points higher than the baseline YOLOv8n network. This represents an 18.2% improvement in detection speed, which demonstrates good generalization ability.
The reliability of YOLOv8-EA in identifying and locating multi-stage tomato fruits in complex environments demonstrates its efficacy in this regard and provides a technical foundation for the development of intelligent tomato picking devices.