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21 May 2018 An unmanned aerial system for the detection of crops with undergraduate project-based learning
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To keep pace with population growth, farmers are leveraging a host of new technologies to improve crop production, including genetically modified organisms (GMOs), along with increased chemical pesticides and fertilizer usage. These new techniques, however, have sometimes led to runoff problems for water systems and local watersheds. By using dronebased technologies the overuse of fertilizers, chemical sprays, and pesticides can be minimized, while preserving farm output and quality. This paper discusses lessons learned from and progress made in a year-long capstone research and development project performed by engineering and computer science students at York College of Pennsylvania. The project involves the study and use of multispectral camera technologies along with drones to survey farms growing corn in various climates. The technologies used to assess farms and modern farming practices are by their nature multidisciplinary. Students involved with this project have thus needed to draw on their engineering and scientific backgrounds while learning new and varied topics to tackle this real-world problem. This paper also examines some of the teaching challenges encountered when using project-based learning (PBL) techniques with engineering students to tackle a multidisciplinary problem similar to the types they will likely face in their professional careers. For example, the students have needed to apply best principles to design and build a drone system to assess crop health. Moreover, they have needed to understand the legal responsibilities of operating drones, farmer issues, and a host of technologies unfamiliar to them prior to this project. Student metrics and outcomes are also assessed to improve the process for future years.
Conference Presentation
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S. A. Wilkerson, A. D. Gadsden, and S. A. Gadsden "An unmanned aerial system for the detection of crops with undergraduate project-based learning", Proc. SPIE 10664, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III, 106640M (21 May 2018);

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