#育苗智能化

综述种子带技术从精准排种向多功能智能播种平台演进的路径与前景。

基于物联网传感器实现农田土壤湿度自动监测与灌溉控制。

镇原县临泾镇建成2座智慧玻璃温室、核心示范124.4亩，配套智能温控、水肥一体化与轨道作业，无土栽培串番茄年总产值可达2600余万元，以“专家+温室+辐射”模式带动设施蔬菜提质增效。

提出物联网结合机器学习的智慧农业系统，实现环境监测与智能灌溉决策。

研制集开沟播种覆土镇压于一体的智能蔬菜播种机并优化排种参数。

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.

山东移动以 5G 为基、AI 为核、数据为翼，打造"云—网—边—端—智"全栈能力，在临沂市沂南县荟馨园农场联合沂南县农业农村局搭建智慧大棚一体化平台。大棚内智能终端密布，24 小时自动采集作物长势等 30 余项数据，通过 5G 网络实时上传至平台；水肥一体机根据作物生长情况精准调配氮磷钾比例，水肥利用率提升 30% 以上、化肥用量减少 25%；虫情监测设备自动识别虫害种类，超标即刻推送预警。山东作为全国重要粮食主产区，山东移动同步在低空智联、农业大模型等领域开展创新实践，推动农业生产从"靠经验"向"靠数据"、从"人力忙"向"智能管"转型。

9月4日,"神农育种Studio"正式发布,数据底座汇集超1000万条农业知识图谱数据、超5000万条农业生产数据。北京将依托农业中关村核心区打造北京农业中关村智能育种创新基地,计划建设智能育种共性技术创新中心和装备创新中心。