As an important and nutrient-rich economic crop, apple is significantly threatened by leaf diseases, which severely impact yield, making the timely and accurate diagnosis and segmentation of these diseases crucial. Traditional segmentation models face challenges such as low segmentation accuracy and excessive model size, limiting their applicability on resource-constrained devices. To address these issues, this study proposes RMP-UNet, an efficient and lightweight model for apple leaf disease segmentation. Based on the traditional UNet architecture, RMP-UNet incorporates an efficient multi-scale attention mechanism (EMA) along with innovative lightweight reparameterization modules (RepECA) and multi-scale feature fusion dynamic upsampling modules (PagDy), optimizing feature extraction and fusion processes to improve segmentation accuracy while reducing model complexity. The experimental results demonstrate that RMP-UNet achieves superior performance compared to mainstream models across multiple metrics, including a mean Intersection over Union (mIoU) of 83.27%, mean pixel accuracy of 89.84%, model size of 9.26 M, and computational complexity of 21.55 G FLOPs, making it suitable for deployment in resource-constrained environments and providing an efficient solution for real-time apple leaf disease diagnosis.

This study aims to address the challenges of apple recognition in complex environments by proposing a solution based on the FC-DETR model. Apple cultivation is an important part of agriculture in Xinjiang, but the increasing shortage of labor has driven the demand for automated harvesting technologies. Therefore, research and development of apple recognition technology in complex environments have become crucial. While CNN architecture models can accurately identify apples in conventional settings, their inherent receptive field limitations prevent them from fully capturing global features in noisy and complex environments, making it difficult to achieve the accuracy and robustness required for practical applications. To solve this problem, this study proposes a real-time object detection model, FC-DETR. This model incorporates the innovative FEMA-BasicBlock residual module, the CAFM cross-scale adaptive feature fusion module, and the novel Inner-WIoU loss function, thereby enhancing feature processing, multi-scale feature selection and integration, and detection accuracy. Experimental results show that the FC-DETR model achieves an apple recognition accuracy of 87 % and a recall rate of 82 % in complex backgrounds while maintaining a lightweight design. This study not only makes significant advances in automated apple harvesting technology but also contributes to improving the efficiency and sustainability of the apple industry in Xinjiang and globally.

To address the challenge of accurate identification in low-texture cattle breeds such as Angus, this study presents a structurally optimized two-stage cattle face re-identification framework tailored for complex farming environments. In the detection stage, a lightweight model named Pruned Edge-enhanced Real-Time Detection Transformer (PrunedEdge-DETR) is proposed to enhance contour sensitivity while significantly reducing computational cost via grouped Taylor pruning. For the recognition stage, a dual-branch feature extraction network called Dual-channel fused MobileViT (DCFuseViT) is designed, which fuses local contours, fine-grained textures, and global semantic features to improve feature discriminability. Additionally, a feature library–based matching mechanism named EffiLibCatReID is introduced to enable fast and scalable identity retrieval. Experimental results on the AngusDataset-128 demonstrate that the proposed method achieves a re-identification accuracy of 97.8% with an average inference time of 9.61 ms per image, confirming its real-time performance and deployment potential. The source code is available at: https://github.com/HLJ11235/cattle-re-id.git.