Improved Tomato Detector Supporting for Automatic Harvesting Systems

Currently, Artificial Intelligence has penetrated every corner of social life. Agriculture is one of the most important fields that attracts a lot of attention from researchers to develop serving tools. This paper focuses on developing a vision-based tomato detector to support robotics and automatic harvesting systems. The main technique is to improve the YOLOv8n network architecture with the entire replacement of the original convolution module with a new convolution module, named the Receptive Field Attention Convolution. The experiment was trained and evaluated on the Laboro Tomato dataset. As a result, the proposed network achieved 88.2% of mAP@0.5 and 45.8% of mAP@0.5:0.95. These results show that the proposed network has better performance than other networks under the same experimental conditions.