#RT-DETR

结合DINOv3与YOLO26提升蔬菜田间作物与杂草检测精度，助力精准除草。

提出一种注意力引导的自适应多尺度融合方法，提升遥感图像目标检测精度。

RTM-KDOS: Keypoint detection and operability scoring for multifunctional tomato harvesting and de-leafing robots。Computers and Electronics in Agriculture

针对养殖场夜间光照不足难题，提出Night-DETR模型识别肉牛行为，提升弱光条件下行为监测精度，为智能畜牧无接触监管提供方法支撑。

9月7日是袁隆平院士诞辰96周年，由湖南省农业科学院牵头联合湖南杂交水稻研究中心、绥宁县人民政府以及相关种业企业，在湖南省邵阳市武阳村开展总面积202亩的超级稻爽两优138大面积制种示范。专家组现场随机抽取3块田开展机械实收测产：三块田平均亩产530.4公斤，创下杂交水稻大面积制种高产新的世界纪录。

中国农业大学黄晨晓、何林然、黄文涛、张晓绚等于Sensors发表TMAF-YOLO模型——基于YOLOv8n的轻量化番茄成熟度与缺陷果实检测模型,隶属中国农业大学120周年校庆专刊。该模型以LGhostConv替换选定卷积结构减少冗余计算;引入番茄成熟度感知聚合融合模块TMAF增强检测分支的颜色、纹理和局部结构特征表示能力;使用MA-CB Focal Loss改善类别不平衡和难样本条件下的学习。实验结果显示,Precision、Recall、mAP50和mAP50-95分别达到0.889、0.873、0.954和0.776,模型仅含2.647M参数,推理速度209.030 FPS,优于YOLOv11n和YOLOv12n,同时保持实时推理性能。

提出基于Swin Transformer的跨域遥感场景分类框架，提升异构传感器间的域不变特征学习。

Inflorescence trimming is essential for producing high-quality table grapes, but it is labor-intensive and demands skilled labor. Automating it is difficult: grape inflorescences have a complex branching structure, are frequently occluded, and are viewed under highly variable illumination, and the cutting tool must be positioned precisely. This study presents perception and motion control methods for a grape inflorescence trimming robot. The methods localize the cutting point on the inflorescence and position the cutting tool at that point, using a dual-range framework. At far range, a detection model identifies the target inflorescence and guides the manipulator to a suitable observation distance; at close range, a segmentation-based method localizes the cutting point on the main stem. This study covers cutting-point localization and tool positioning only. The robot stopped when the scissors reached the cutting point; the stem was not cut, so the trimming result itself was not evaluated. To improve perception robustness, multi-frame detection and real-time illumination augmentation were introduced. Two motion control strategies were developed and compared: sequential trajectory control (STC) and hybrid feedback control (HFC), the latter using real-time visual feedback for continuous motion correction. The system was evaluated in a greenhouse vineyard on 30 preselected inflorescences of the table grape cultivar Shine Muscat grown under a pergola training system. The mobile platform was positioned manually in front of each target. Each inflorescence was processed once by each control strategy, yielding 60 trials in total. Positioning was counted as successful when the main stem was between the scissor blades and within the effective cutting range. STC achieved a positioning success rate of 90.0 %, a mean positional error of 2.1 cm, and a mean operation time of 31.9 s. HFC achieved a positioning success rate of 83.3 %, a smaller mean positional error of 1.4 cm, and a longer mean operation time of 35.4 s. In successful trials, HFC placed the scissors where a cut would keep exactly the two uppermost lateral branches in 52.0 % of cases, compared with 33.3 % for STC. Visual feedback therefore reduced positioning error but did not increase positioning success or shorten operation time. Real-time illumination augmentation raised the target detection rate from 41.6 % to 100.0 % across 125 frames covering cloudy and sunny conditions at multiple locations in the vineyard, and multi-frame aggregation eliminated the 3.0 % single-frame target selection error. Perception took 58.5 % of the total operation time under STC and was the main time bottleneck. These results show that the proposed methods can localize the cutting point and position the cutting tool on a single cultivar in a greenhouse vineyard under natural illumination and occlusion.