Journal of Agricultural Big Data >
Tomato Object Detection Algorithm Based on YOLOv8
Received date: 2024-10-12
Accepted date: 2025-04-01
Online published: 2025-09-28
With the acceleration of the process of agricultural intelligence, the application of artificial intelligence technologies based on deep learning and robotics in agricultural production has attracted more and more attention. In order to solve the problems of high false recognition rate, low positioning accuracy and low picking efficiency of existing tomato fruit recognition methods in complex environments, an improved YOLOv8 network model was proposed to improve the detection accuracy and speed of automatic tomato fruit picking. The network takes YOLOv8 as the initial model, and adds the Deformable Convolution Module (DCN) to its backbone network, which effectively improves the detection accuracy of the model for small targets and reduces the missed detection rate. The SE attention mechanism module was introduced on the Neck side to improve the attention to the detection target. The Inner-IoU loss function is used to replace the original CIoU loss function to improve the regression accuracy of the bounding box in object detection. In this study, the average accuracy of the improved YOLOv8 model was increased by 7.2, 6.4, 6.6, and 7.7 percentage points compared with the SSD, YOLOv4, YOLOv5, and YOLOv7 network models, respectively, and the accuracy of the improved YOLOv8 model increased by 3.8%, the recall rate increased by 0.6%, and the mAP@0.5 and mAP@[0.5:0.95] increased by about 2.6% and 1.9%, respectively. The results show that the improved YOLOv8 model can effectively improve the accuracy and speed of automatic picking and detection of tomato fruits, which is of great significance for the realization of automatic picking of tomatoes.
WU Dan , MA XiaoJun , LIU DeSheng , SONG Wei , SU WenXian . Tomato Object Detection Algorithm Based on YOLOv8[J]. Journal of Agricultural Big Data, 2025 , 7(3) : 281 -293 . DOI: 10.19788/j.issn.2096-6369.000075
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