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

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.

无人机技术和航空成像可以帮助农民满足未来的需求，在不使用更多土地的情况下提高作物产量并降低成本。 然而，获得技术、实地以及飞行和捕获图像的许可证可能会限制开发人员的数量。 该项目设计了基准，用于捕获、表征和共享从部署在农业环境中的自主无人驾驶飞行器（AUAV）系统收集的经验数据。 它在俄亥俄州州TDA数据共享空间上管理和共享AUAV数据，这将使跨学科研究成为可能，并将举办云计算农业分析研讨会，吸引计算机科学家，地球科学家，农民和农业工程师。一个具有启发性的应用示例是作物间伐任务，通过选择性地向杂草和弱作物施用除草剂来防止植物之间的竞争。人工间伐对体力要求很高，每英亩的成本高达100美元。用于作物间伐的自主无人机系统需要能够处理图像，以识别各种植物类型的拥挤程度。如果检测到过度拥挤，AUAV可以降低并悬停以捕获适合于强作物、弱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，该系统可以检测作物稀疏，捕获低分辨率和高分辨率图像。研究人员可以模拟这种AUAV系统，使用不同的硬件和软件进行整体测试，并使用这些基准数据来探索在不进入农田的情况下对拥挤和作物强度进行机载或在线分类的方法。 还可以记录AUAV功率，以帮助探索充电与采样较少数据的决策。

项目成果

Adaptive autonomous UAV scouting for rice lodging assessment using edge computing with deep learning EDANet

• DOI: 10.1016/j.compag.2020.105817
• 发表时间: 2020-12-01
• 期刊: COMPUTERS AND ELECTRONICS IN AGRICULTURE
• 影响因子: 8.3
• 作者: Yang, Ming-Der;Boubin, Jayson G.;Stewart, Christopher C.
• 通讯作者: Stewart, Christopher C.

Assessing the efficacy of machine learning techniques to characterize soybean defoliation from unmanned aerial vehicles

• DOI: 10.1016/j.compag.2021.106682
• 发表时间: 2022-01-20
• 期刊: COMPUTERS AND ELECTRONICS IN AGRICULTURE
• 影响因子: 8.3
• 作者: Zhang, Zichen;Khanal, Sami;Stewart, Christopher
• 通讯作者: Stewart, Christopher

Fast inference services for alternative deep learning structures

• DOI: 10.1145/3318216.3363331
• 发表时间: 2019-11
• 期刊: Proceedings of the 4th ACM/IEEE Symposium on Edge Computing
• 作者: Eduardo Romero;Christopher Stewart;Nathaniel Morris

Revisiting Online Scheduling for AI-DrivenInternet of Things

重新审视人工智能驱动的物联网在线调度

• 发表时间: 2019
• 期刊: ACM Symposium on Edge Computing
• 作者: Babu, Naveen;Stewart, Christopher
• 通讯作者: Stewart, Christopher