In the intelligent tomato-picking scenario, challenges such as insufficient accuracy in recognizing the growth pose of target tomatoes and inaccurate positioning of picking and grasping points have led to low efficiency in automated picking. To address these issues, this paper introduces an object detection optimization model based on Yolov8s, termed YOLOv8S-ECC. The model focuses on “Judging tomato pose by the spatial vector of the relative position between the calyx and the center point of the fruit,” aiming to enhance high-precision positioning of both the tomato calyx and fruit, thereby laying the groundwork for subsequent pose judgment and picking point positioning. We have integrated the ECA (Efficient Channel Attention) and Coordinate attention mechanisms into the Backbone network and introduced the CBAM (Convolutional Block Attention Module) attention mechanism into the Neck network. The combined effect of these attention mechanisms effectively overcomes the recognition challenges posed by the calyx’s color texture, which closely resembles the environment. This integration has also enhanced the model’s robustness in complex field environments. Test results indicate significant improvements: the accuracy rate, recall rate, and mAP@50 for detecting tomato fruits and calyces are 81.7% and 87.5%, 92.7% and 85.9%, and 89.7% and 91.3%, respectively, compared to the original model. By encapsulating the algorithm and integrating it with the picking robot, tests in a simulated environment (different lighting conditions and foliage occlusion situations) show picking success rates of 93.02%, with an average picking operation time of 14.2 ± 0.855 s, including an image recognition and processing time of 0.035 s. This research offers an effective technical solution for high-precision visual perception and pose judgment in fruit and vegetable picking robots, contributing to improved quality in tomato industry picking operations.

To address the dual challenges of discriminating weeds from rice seedlings for precision weed management operations, such as targeted spraying and robotic weeding, in complex paddy-field scenes and deploying high-precision models on resource-limited edge devices, we propose DGS-Net, a deformable attention, GSConv-based feature fusion, and SEAM-enhanced lightweight network based on YOLOv11n. The backbone incorporates a convolutional block with parallel split attention and deformable attention transformer (C2PSA_DAT) module to improve the extraction of irregular and fine-grained weed features, the neck integrates a VoV-GSCSP module to enable lightweight multi-scale feature fusion for small and densely distributed targets, and a separated and enhancement attention module (SEAM) is placed before the detection head to enhance robustness under leaf occlusion and complex paddy-field background interference. In comparative experiments conducted on the paddy-field dataset under unified training and evaluation settings, DGS-Net achieved 91.7% precision, 86.8% recall, and 92.4% mean average precision (mAP), with a model size of 5.8 MB and a computational cost of 6.2 giga floating-point operations (GFLOPs). Compared with representative lightweight baseline detectors, DGS-Net showed a more favorable balance between detection accuracy and deployment efficiency. In additional edge-device deployment tests using the test set, the model sustained real-time inference at 32.5 FPS and achieved mAP@0.5, precision, and recall of approximately 0.928, 0.919, and 0.867, respectively. Overall, DGS-Net improves irregular feature extraction, enables lightweight multi-scale feature fusion, and increases robustness to occlusion while retaining strong deployability. The method therefore provides practical visual-perception support for precise, real-time crop–weed discrimination and precision weed management in complex paddy-field environments.