Validation of High-Definition Maps Using Agricultural and Autonomous Vehicles

农业数字技术的进步使高分辨率作物性能数据捕获成为可能，为改进决策提供了巨大的洞察力。然而，现有的农业技术解决方案大多是单独开发的，没有与其他解决方案集成，这给最终用户带来了挫折，减缓了采用速度。该项目将使用高精度的RTK设备来绘制葡萄园内的所有要素，包括：入口、道路、排水沟、灌溉歧管、传感器、柱子和葡萄藤；并将这些数据转换为开源地图。这些地图的主要用户是机器人和无人机供应商，他们依赖详细的地理空间信息来安全可靠地运营他们的平台。直接访问单一的统一地图将消除昂贵的单独调查。这将是新兴自主技术的关键推动因素，也是采用这些技术的关键。虽然该项目的重点是葡萄园，但这将同样适用于其他行作物(灌木水果、草莓、啤酒花、果园、高价值蔬菜)。不像可耕种作物，本质上主要是2D的，因此使用通过shapefile共享的相对简单的技术来绘制地图，目前没有行作物的描述性地图方法。这一限制意味着种植者无法轻松部署尖端农业技术来监测作物变异和问题。该项目将调查核心测绘技术和技术协作，但仍有扩展的余地，以调查其他方法，验证跨更多技术平台的导航地图，以捕获更有洞察力的虫害、疾病或杂草、产量和树冠结构的数据。

Digital technology advances for agriculture have enabled high-resolution crop performance data capture, offering great insight for improved decision-making. However, existing Agri-tech solutions have largely been developed in isolation, do not integrate with other solutions, causing frustration to end-users, slowing adoption.The project will utilise highly accurate RTK equipment to map all features within the vineyard including: entrances, roadways, drains, irrigation manifolds, sensors, posts, and vines; and convert this data into open-source maps. Key users of these maps are robotics and UAV suppliers, who rely upon detailed geospatial information to safely and reliably operate their platforms. Direct access to a single unified map would eliminate costly separate surveys. This will be a key enabler to emerging autonomous technologies and will be critical to their uptake. Although the project focuses on vineyards, this will be equally applicable in other row crops (bush fruit, strawberries, hops, orchards, high value vegetables).Unlike arable cropping which is largely 2D in nature and therefore mapped using relatively simple techniques shared via shapefiles, there are no current descriptive mapping methods for row crops. This limitation means growers are unable to easily deploy cutting-edge agri-tech to monitor crop variation and issues. The project will investigate core mapping techniques and technology collaboration, but there is scope to extend it to investigate additional methods, validating the navigational maps across more technology platforms to capture more insightful data of pests, disease or weeds, yield, and canopy structure.