UAV Farm Health Index Map | 无人机农田健康指数图生成

Using multispectral or high-resolution RGB cameras mounted on agricultural UAVs to capture orthophotos or mosaics of farmland, AI models compute vegetation indices (e.g., NDVI, NDRE) and generate a farm health-index heatmap, where colors distinguish crop vigor (red = poor, yellow = medium, green = healthy). This skill quickly identifies problem zones (e.g., nutrient/water deficiency, pests/disease, weeds) and guides precision variable-rate fertilization and crop-protection operations. Application scenarios: large-scale farms, agricultural cooperatives, drone crop-protection services, agricultural research. After the UAV flight uploads imagery, the system automatically produces a health-index map, outputs coordinates and area of problem zones, and pushes suggestions (e.g., 'NDVI is low in the northeast corner, recommend on-site pest inspection'). Skill features: traditional manual field scouting is slow and tends to miss early stress. UAV-based health-index heatmaps drastically improve monitoring efficiency, enable precise variable-rate operations, and save agro-inputs. A core technology of smart agriculture. | 通过农业无人机平台搭载的多光谱或高分辨率RGB相机，采集农田的正射影像或拼接图，利用AI模型计算植被指数（如归一化植被指数NDVI、归一化红边指数NDRE等），生成农田健康指数热力图，用颜色区分作物长势（红色代表健康差、黄色代表中等、绿色代表健康）。该技能可快速识别问题区域（如缺肥、缺水、病虫害、杂草），指导精准变量施肥或植保作业。应用场景：规模化农场、农业合作社、植保无人机服务、农业科研。无人机飞行后上传影像，系统自动生成健康指数图，输出问题区域的坐标和面积，并推送建议（如'东北角区域NDVI偏低，建议实地检查虫害'）。技能特点：传统农田巡查依赖人工，效率低且难以发现早期胁迫。通过无人机快速生成健康指数热力图，可大幅提高监测效率，实现精准农业变量作业，节省农药肥料。该技能是智慧农业的核心技术之一。

by @18072937735 (smyx-skills)·MIT-0