Tomato ripeness detection and fruit segmentation based on instance segmentation

• Published in: Frontiers in plant science Vol. 16; p. 1503256
• Main Authors: Wei, Jinfan, Sun, Yu, Luo, Lan, Ni, Lingyun, Chen, Mengchao, You, Minghui, Mu, Ye, Gong, He
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 2025
• Subjects: ACP-tomato-seg; adaptive feature extraction; complex field environments; multi-scale features; self-attention mechanism; tomato instance segmentation; self-attention mechanism; complex field environments; multi-scale features; tomato instance segmentation; adaptive feature extraction; ACP-tomato-seg
• DOI: 10.3389/fpls.2025.1503256
• ISSN: 1664-462X

In order to meet the urgent need of fruit contour information for robot precision picking in complex field environments (such as light changes, occlusion and fruit overlap, etc.), this paper proposes an improved YOLOv8s-seg method for tomato instance segmentation, named ACP-Tomato-Seg. The method proposes two innovative modules: the Adaptive and Oriented Feature Refinement module (AOFRM) and the Custom Multi-scale Pooling module (CMPRD) with Residuals and Depth. By deformable convolution and multi-directional asymmetric convolution, the AOFRM module adaptively extracts the shape and direction features of tomatoes to solve the problems of occlusion and overlap. The CMPRD module uses the pooling kernels of self-defined size to extract multi-scale features, which enhances the model’s ability to distinguish tomatoes of different sizes and maturity levels. In addition, this paper also introduces a partial self-attention module (PSA), which combines channel attention and spatial attention mechanism to capture global context information, improve the model’s ability to focus on the target region and extract details. To verify the validity of the method, a dataset of 1061 images of large and small tomatoes was constructed, covering six ripened categories of large and small tomatoes. The experimental results show that compared with the original YOLOv8s-seg model, the performance of ACP-TomatoSeg model is significantly improved. In the bounding box task, mAP50 and MAP50-95 are improved by 5.6% and 8.3%, respectively, In the mask task, mAP50 and MAP50-95 increased by 5.8% and 8.5%, respectively. Furthermore, additional validation on the public strawberry instance segmentation dataset (StrawDI_Db1) indicates that ACP-Tomato-Seg not only exhibits superior performance but also significantly outperforms existing comparative methods in key metrics. This validates its commendable generalization ability and robustness. The method showcases its superiority in tomato maturity detection and fruit segmentation, thus providing an effective approach to achieving precise picking.