TOMATO MATURITY DETECTION BASED ON IMPROVED YOLOv8n

摘要

The detection of tomatoes for automatic picking is challenging due to the dense distribution of fruit and severe occlusions. To address this, a dataset is developed using tomato images captured in a greenhouse environment, and an enhanced model for tomato fruit maturity detection based on YOLOv8n is proposed, which incorporates the EMA attention mechanism and the C2f-Faster module for multi-scale feature fusion. These additions not only improve detection accuracy but also enhance detection speed, thereby boosting the model's robustness and generalization ability. Experimental results demonstrate that the proposed ECF-YOLOv8n model achieves detection accuracies of 93.8%, 94.7%, 92.5% and 94.1% for immature, nearly mature, ripe tomatoes and mean average precision in a greenhouse setting, respectively. The model's size is 4.7 MB, with GFLOPs of 6.5G. Compared to advanced models like RT-DETR, YOLOv5, YOLOv7 and YOLOV11, the ECF-YOLOv8n model outperforms them in both detection accuracy and speed. This work provides valuable insights for the research, development and optimization of tomato picking robots.