#组培种苗定植机器人

浙江省农业科学院农业装备研究所研发组培种苗定植机器人,借视觉与力反馈实现丝兰、马铃薯苗自动定植,位姿识别精度超95%,单株5—7秒、效率为人工3—4倍、损伤率低于5%。

研发并评估链槽式侧向供苗装置，实现油菜裸根苗多行移栽的模块化步进错位供苗。

果蔬耕种管收空地协同机器人实现种管收全程无人化作业，耐渍育种机器人每日可采集2万条以上植物表型数据。

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.

工业和信息化部、农业农村部发布《农业领域机器人典型应用场景名单》公示，共55个场景拟入选，湖北省申报的果蔬耕种管收空地协同机器人、水稻（小麦）耐渍种质鉴定和育种机器人两个场景上榜。果蔬场景由襄阳金美科林联合湖北省农科院经济作物研究所、湖北文理学院打造，在设施农业、露地大田、丘陵山区三类地形部署系列化装备，贯穿耕整、播种移栽、施药除草、巡检、采摘、运输六环节，整体作业效率提升130%以上，水资源利用率提高70%、农药使用量减少30%、亩均产值较传统种植提升5倍。耐渍育种机器人依托全国首个耐渍育种生境模拟机器人，每日可采集2万条以上表型数据，效率较人工提升约100倍。

10月26日第四届林木表型组学前沿论坛在南京举行，江苏省农科院无锡分院助理研究员张嘉懿作《表型获取到智能决策》报告，展示“青禾守望者”农情智能监测机器人在长势监测、产量预报与氮肥调控中的应用，构建功能—结构协同模拟模型支撑农业数字孪生。

芦笋田垄间，机器人往来穿梭，采收、转运、分拣环环相扣。近日，由农业农村部南京农业机械化研究所农业机器人创新团队研制的芦笋智能采收机器人、多足转运机器人、芦笋智能分拣机器人集中亮相，在田间...

工信部、农业农村部公示《农业领域机器人典型应用场景名单》，湖北申报的“果蔬耕种管收空地协同机器人”（省农科院经作所等）与“水稻(小麦)耐渍种质鉴定和育种机器人”（省农科院粮作所等）上榜。前者整体作业效率提升130%、亩均产值提升5倍；后者每日采集2万条以上表型数据，效率较人工提升约100倍。