Weed management plays a crucial role in the growth and yield of lettuce, with timely and effective weed control significantly enhancing production. However, the increasing labor costs and the detrimental environmental impact of chemical herbicides have posed serious challenges to the development of lettuce farming. Mechanical weeding has emerged as an effective solution to address these issues. In precision agriculture, the prerequisite for autonomous weeding is the accurate identification, classification, and localization of lettuce and weeds. This study used an intelligent mechanical intra-row lettuce-weeding system based on a vision system, integrating the newly proposed LettWd-YOLOv8l model for lettuce–weed recognition and lettuce localization. The proposed LettWd-YOLOv8l model was compared with other YOLOv8 series and YOLOv10 series models in terms of performance, and the experimental results demonstrated its superior performance in precision, recall, F1-score, mAP50, and mAP95, achieving 99.732%, 99.907%, 99.500%, 99.500%, and 98.995%, respectively. Additionally, the mechanical component of the autonomous intra-row lettuce-weeding system, consisting of an oscillating pneumatic mechanism, effectively performs intra-row weeding. The system successfully completed lettuce localization tasks with an accuracy of 89.273% at a speed of 3.28 km/h and achieved a weeding rate of 83.729% for intra-row weed removal. This integration of LettWd-YOLOv8l and a robust mechanical system ensures efficient and precise weed control in lettuce cultivation.

Pepper is a vital crop with extensive agricultural and industrial applications. Accurate phenotypic measurement, including plant height and stem diameter, is essential for assessing yield and quality, yet manual measurement is time-consuming and labor-intensive. This study proposes a deep learning-based phenotypic measurement method for peppers. A Pepper-mini dataset was constructed using offline augmentation. To address challenges in multi-plant growth environments, an improved YOLOX-tiny detection model incorporating a CA attention mechanism was developed, achieving a mAP of 95.16%. A detection box filtering method based on Euclidean distance was introduced to identify target plants. Further processing using HSV threshold segmentation, morphological operations, and connected component denoising enabled accurate region selection. Measurement algorithms were then applied, yielding high correlations with true values: $R^2$ = 0.973 for plant height and $R^2$ = 0.842 for stem diameter, with average errors of 0.443 cm and 0.0765 mm, respectively. This approach demonstrates a robust and efficient solution for automated phenotypic analysis in pepper cultivation.

Timely fish disease monitoring is an important requirement for sustainable aquaculture because disease outbreaks can reduce survival, increase treatment inputs, and destabilise production. In aquaponic systems, fish health is also linked to nutrient cycling and the stability of integrated fish–vegetable production, making automated fish-health perception a potentially useful component of resource-efficient farming. Existing classification and detection methods can identify disease categories or approximate lesion locations, but they provide limited information about lesion area, boundary shape, and severity-related spatial extent. This study presents a deep learning framework for pixel-level fish lesion segmentation to support sustainable aquaculture health monitoring, with aquaponic systems considered as a potential application context. The framework combines lesion-preserving frequency augmentation (LPFA), confidence-guided large-kernel encoding (CGLE), and confidence-filtered decoder refinement (CFDR). LPFA expands lesion appearance variation during training while retaining the main lesion layout. CGLE uses coarse prediction confidence to allocate broader contextual modelling to uncertain encoder regions, and CFDR applies selective decoder correction to low-confidence regions. A public freshwater fish disease dataset is reformulated into a dense prediction task with 1750 raw images from seven image-level categories, including six disease categories and one normal healthy category. The images are divided into training, validation, and test subsets at an 8:1:1 ratio, and controlled augmentation strategies are applied online rather than being used to create a larger static dataset. Across five random-seed runs, the proposed method achieves $82.6\pm 0.3$% mIoU, $90.9\pm 0.2$% mDice, and $73.5\pm 0.4$% Boundary IoU. Relative to TransUNet, the mean mIoU rises from $78.4\pm 0.4$% to $82.6\pm 0.3$%, and Boundary IoU rises from $68.8\pm 0.5$% to $73.5\pm 0.4$%, with paired bootstrap testing supporting the stability of the improvement. These results indicate its potential as a lesion-quantification decision-support component for smart and sustainable fish-production systems.