Crop disease has been a severe issue for agriculture, causing economic loss for growers. Thus, disease identification urgently needs to be addressed, especially for precision agriculture. As of today, deep learning has been widely used for crop disease identification combined with optical imaging sensors. In this study, a lightweight convolutional neural network model is designed and validated on two publicly available imaging datasets and one self-built dataset with 28 types of leaf and leaf disease images of 6 crops as the research object. This model is an improvement of the existing convolutional neural network, reducing the floating-point operations by 65%. In addition, dilated depth-wise convolutions were used to increase the network receptive field and improve the model recognition accuracy without affecting the network computational speed. Meanwhile, two attention mechanisms are optimized to reduce attention module computation, improving the capability of the model to select the correct regions of interest. After training, this model achieved an average accuracy of 99.86%, and the image calculation speed was 0.173 s. Comparing with 11 backbone models and 5 latest crop leaf disease identification studies, the proposed model achieved the highest accuracy. Therefore, this model with an advantage of balancing between the calculation speed and recognition accuracy. Furthermore, the proposed model provides a theoretical basis and technical support for the practical application and mobile terminal applications of crop disease recognition in precision agriculture.

Crop seedlings are similar in appearance to weeds, making crop detection extremely difficult. To solve the problem of detecting crop seedlings in complex field environments, a seedling dataset with four crops was constructed in this study. The single leaf labeling method was proposed as an alternative to conventional labeling approaches to improve the detection accuracy for dense planting crops. Second, a seedling detection network based on YOLOv5 and a transformer mechanism was proposed, and the effects of three features (query, key and value) in the transformer mechanism on the detection accuracy were explored in detail. Finally, the seedling detection network was optimized into a lightweight network. The experimental results show that application of the single leaf labeling method could improve the mAP0.5 of the model by 1.2% and effectively solve the problem of missed detection. By adding the transformer mechanism module, the mAP0.5 was improved by 1.5%, enhancing the detection capability of the model for dense and obscured targets. In the end, this study found that query features had the least impact on the transformer mechanism, and the optimized model improved the computation speed by 23 ms·frame⁻¹ on the intelligent computing platform Jetson TX2, providing a theoretical basis and technical support for real-time seedling management.

Node recognition is essential for robotic winter pruning of grapevines, but deep learning models may miss occluded or indistinct nodes, leading to incorrect pruning positions. This study proposes an interpretable node re-recognition method based on homography transformation to improve pruning position identification in horizontal trellis grapevines. Canopy images captured from three views were instance segmented using YOLO11l-seg. A crossed annotation strategy was introduced to annotate occluded cane regions as continuous instances, facilitating subsequent grape cane restoration. Nodes that were difficult to recognize in the middle target image became more visible in the two side reference images due to camera view changes. Their positions were then estimated in the target image using homography matrices calculated from cane key points detected and matched by SuperPoint and LightGlue. Results showed that YOLO11l-seg achieved the best segmentation performance, with a mAP of 86.9%. Although the crossed annotation strategy slightly reduced average segmentation accuracy from 87.6% to 86.9%, it reduced cane restoration difficulty. Compared with translation transformation, the homography-based method reduced the average node position offset by 15.4 pixels and achieved a re-recognition success rate of 86.7%. The pruning position identification accuracy increased from 72.9% to 89.3%. The AI contribution lies in comparing annotation strategies and applying deep learning-based keypoint detection and matching to backlit, weakly textured grape canes, while the engineering contribution lies in homography-based node re-recognition for robotic pruning position identification. These results indicate that the proposed node re-recognition method is promising for robotic winter pruning of grapevine.

Crops cultivated on trellis systems, such as kiwifruit, grapes, et al., create a partially structured workspace; this environment is highly suitable for Cartesian robotic harvesting. However, limited extension range of current Cartesian manipulators necessitates a large vertical space, which directly conflicts with the height constraints of trellis canopies. This paper presented a hollow telescopic Cartesian manipulator with a belt-driven cascaded differential transmission for single-degree-of-freedom kiwifruit operations. The two-stage nested carbon fiber structure offers an extension ratio of 1.83:1 and a 549 mm retracted length, requiring 45.36% less vertical space than its single-stage architecture. The hybrid model–vision error compensation (HMVEC) strategy is proposed to address nonlinear positioning errors inherent in the cantilever telescopic configuration. It combines a polynomial–Fourier kinematic model for feedforward correction with YOLO11n AprilTag detection. The results showed the HMVEC strategy reduced the positioning root mean square error (RMSE) by 42.28% (from 7.90 mm to 4.56 mm). The designed manipulator achieved a 95% kiwifruit-transfer success rate at a mean cycle time of 4.8 s per fruit. These results demonstrate the feasibility of the proposed structural design and HMVEC strategy for precise manipulator positioning and post-detachment fruit transfer.