A tomato automatic detection method based on an improved YOLOv8s model is proposed to address the low automation level in tomato harvesting in agriculture. The proposed method provides technical support for the automatic harvesting and classification of tomatoes in agricultural production activities. The proposed method has three key components. Firstly, the depthwise separable convolution (DSConv) technique replaces the ordinary convolution, which reduces the computational complexity by generating a large number of feature maps with a small amount of calculation. Secondly, the dual-path attention gate module (DPAG) is designed to improve the model’s detection precision in complex environments by enhancing the network’s ability to distinguish between tomatoes and the background. Thirdly, the feature enhancement module (FEM) is added to highlight the target details, prevent the loss of effective features, and improve detection precision. We built, trained, and tested the tomato dataset, which included 3098 images and 3 classes. The proposed algorithm’s performance was evaluated by comparison with the SSD, faster R-CNN, YOLOv4, YOLOv5, and YOLOv7 algorithms. Precision, recall rate, and mAP (mean average precision) were used for evaluation. The test results show that the improved YOLOv8s network has a lower loss and 93.4% mAP on this dataset. This improvement is a 1.5% increase compared to before the improvement. The precision increased by 2%, and the recall rate increased by 0.8%. Moreover, the proposed algorithm significantly reduced the model size from 22 M to 16 M, while achieving a detection speed of 138.8 FPS, which satisfies the real-time detection requirement. The proposed method strikes a balance between model size and detection precision, enabling it to meet agriculture’s tomato detection requirements. The research model in this paper will provide technical support for a tomato picking robot to ensure the fast and accurate operation of the picking robot.

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Diffusion geometry has been adopted in various shape processing applications, ranging from pattern recognition to more recent 3D shape analysis. But scaling factors have a great influence on the results of non-rigid shape processing such as shape retrieval, correspondence and comparison. There remains a difficult challenge for shape processing without a priori knowledge of the scale of the input shapes. In this paper we address the scale ambiguity problem with a new distance measure called Scale-invariant Diffusion Distance (SIDD). This SIDD is the extension of the diffusion distance, and has all the properties inheriting from it. Comparing to some existing distances, the scale-invariant diffusion distance is more suitable for the non-rigid shape analysis. Moreover, the proposed algorithm is simple and easily implementable. The proof of theory is given and some experiments are done on the TOSCA dataset. The results of the experiments show that our method achieves good robustness and effectiveness in scaled shape analysis.

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