#智能分拣

综述水下光学图像中的小目标检测，梳理问题、数据集、算法，并展望未来方向。

提出改进YOLOv8n模型SDSI-YOLO，实现鲜茶叶粘连叶簇的高精度检测。

The growth status of tomato plug-tray seedlings varies continuously, with ambiguous boundaries between grades. Existing methods primarily focus on object detection and coarse-grained classification, making it difficult to fully exploit instance-level visual evidence for fine-grained seedling status assessment and interpretable sorting decisions. To address these limitations, this study proposes a detection-aware RGB-D vision–language framework. An RGB-D instance-aware detection model, RDIA-RTDETR, is developed to integrate RGB appearance and depth geometry through bidirectional cross-attention, enhancing the joint representation of leaf morphology, canopy structure, and plug-cell spatial characteristics to improve the detection accuracy of empty cells, weak seedlings, and Strong seedling. A Detection-aware Semantic Bridge (DSB) module is designed to transform object locations, class confidence scores, and detector query features into instance-level conditioning information, establishing a connection between object detection and subsequent semantic reasoning. Furthermore, a Detection-Aware Structured Seedling-status Report Generator (DA-SSRG) is proposed to integrate individual-seedling visual features with detection-bridge features through gated fusion. By incorporating hierarchical discrimination and risk-sensitive constraints, the model performs five-level seedling status grading and sorting decisions while generating corresponding diagnostic reasons and management recommendations. Experimental results show that RDIA-RTDETR achieves an F1-Score of 95.8% and an mAP50 of 97.3%, representing a 4.6-percentage-point improvement in F1-Score over RT-DETR-MM. The complete framework achieves accuracies of 92.73% and 94.17% for fine-grained grading and sorting decisions, respectively, with a Cause Score of 88.2%, a Suggestion Score of 91.5%, and an overall semantic consistency of 93.6%. The missed-identification rates for severely weak seedlings and samples requiring replacement are reduced to 2.3% and 1.0%, respectively. A web-based prototype enables structured report generation for individual plug cells and seedling status summarization at the tray level, providing technical support for abnormality screening, localization of cells requiring seedling replenishment, and fine-grained sorting decisions in plug-tray seedling production.

提出CNN框架对水果和作物叶片进行五级品质分级与缺陷识别。

设计基于YOLO11n的辣椒种子逐粒精检分选机，实现高通量在线检测与无损剔除。

提出一种结合深度学习和几何建模的猫姿态自动分析框架，从RGB图像提取临床可解释的姿态描述符。

《山西省智慧农业行动计划（2026—2030年）》细化落地举措，围绕丘陵山地特点推进智慧农业先行先试、智慧农（牧、渔）场建设与技术创新。全省坡耕地1686.66万亩、占耕地总面积29.06%，为此加强丘陵山区智能农机研发引进，开展丘陵山区杂粮农机具研究，组建省级智慧农业产业技术体系。

提出YOLOv8n-Light轻量化模型，实现橙橘柚三种柑橘果实泛化识别与定位，并部署于树莓派和采摘