IIS: EAGER: Benchmarks for Autonomous Unmanned Aerial Vehicles in Agriculture Applications

IIS：EAGER：农业应用中自主无人机的基准

• 批准号: 1749501
• 负责人: Christopher Stewart
• 金额: $ 22.5万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-12-15 至 2021-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1749501&HistoricalAwards=false
• 关键词: IIS; EAGER; Benchmarks; Autonomous; Unmanned; IIS; EAGER; Benchmarks; Autonomous; Unmanned

Drone technology and aerial imagery can be developed to help farmers meet future demands to increase crop yield and lower costs without using more land. Access to the technology, to the fields, and to permits to fly and capture imagery can limit the pool of developers, however. This project devises benchmarks that capture, characterize and share empirical data collected from autonomous unmanned aerial vehicle (AUAV) systems deployed in agricultural settings. It curates and shares AUAV data on the Ohio State TDA Data Commons which will enable research across disciplines, and will host an Agriculture Analysis in the Cloud workshop which attracts computer scientists, geoscientists, farmers and agricultural engineers.A revealing example of application is the task of crop thinning, which prevents competition between plants by applying herbicide selectively to weeds and weak crops. Manual thinning is physically demanding and can cost up to $100 per acre. An autonomous UAV system for crop thinning will need to be able to process imagery to identify crowding in various plant types. If over-crowding is detected, the AUAV can lower and hover to capture hi-res images suitable for classification of strong crops, weak crops and weeds. This project creates a reference implementation of an AUAV systems that detects crop thinning, capturing both low and high resolution imagery. Researchers can mimic this AUAV system to perform holistic tests with different hardware and software, and use this benchmarked data to explore approaches for on-board or on-line classification of crowding and crop strength without access to farmland for flying AUAVs. AUAV power can also be recorded, to help explore decisions on charging vs sampling less data.

可以开发无人机技术和空中图像，以帮助农民满足未来的需求，以提高农作物产量并降低成本，而无需使用更多的土地。 但是，访问技术，进入领域以及允许飞行和捕获图像的允许可以限制开发人员的池。 该项目设计了从农业环境中部署的自动无人驾驶汽车（AUAV）系统收集的捕获，表征和共享经验数据的基准。 它策划并分享了俄亥俄州立TDA数据共享的AUAV数据，该数据将在跨学科中进行研究，并将在云研讨会上进行农业分析，吸引了计算机科学家，地球科学家，农民和农业工程师。揭示的示例是施用的示例，是作物稀疏的任务，可以通过施加植物之间的竞争，可以通过选择雄性杂草和杂草来涂抹植物之间的竞争。手动稀疏性的身体要求是每英亩的100美元。用于农作物稀疏的自动无人机系统将需要能够处理图像以识别各种植物类型的拥挤。如果检测到过度拥挤，则AUAV可以降低并悬停以捕获适合于强农作物，弱农作物和杂草分类的高分辨率图像。 该项目创建了AUAV系统的参考实现，该系统检测到作物变薄，捕获低分辨率和高分辨率图像。研究人员可以模仿这个AUAV系统，以使用不同的硬件和软件进行整体测试，并使用此基准数据来探索在船上或在线分类拥挤和作物强度的方法，而无需访问农田飞行AUAVS。 还可以记录AUAV功率，以帮助探索有关收费与采样较少数据的决策。