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

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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.