{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/138165"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/138165","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Path planning of agricultural UAVs for combined coverage and spot spraying application","abstract":"This thesis presents the development of a path planning algorithm for unmanned aerial vehicles (UAVs) to enhance the precision and efficiency of agricultural weed management through combined spot and blanket spraying. Motivated by the growing demand for sustainable and resource-efficient farming, the proposed approach leverages unsupervised clustering, spatial statistics, and optimization techniques to identify weed-dense regions and minimize non-target spraying. Incorporating Density-Based Spatial Clustering of Applications with Noise (DBSCAN) with principal component analysis (PCA) and adapting the traveling salesman problem (TSP) algorithms, the system generates optimized UAV trajectories that reduce operational time, chemical usage, and ecological impact. The algorithm demonstrates that the integrated path planning methodology achieves less than 1.5 times the optimal route. Simulation and field datasets show a significant reduction in non-target spraying while maintaining weed knockdown efficacy better than conventional blanket spray approaches. Additionally, the framework addresses practical UAV constraints, including coverage limits, and adaptability for heterogeneous field conditions. This research presents a scalable solution for autonomous agricultural spraying, enabling data-driven farm management, promoting sustainable crop production, and aligning with global trends in precision agriculture and agricultural automation.","abstract_html":"This thesis presents the development of a path planning algorithm for unmanned aerial vehicles (UAVs) to enhance the precision and efficiency of agricultural weed management through combined spot and blanket spraying. Motivated by the growing demand for sustainable and resource-efficient farming, the proposed approach leverages unsupervised clustering, spatial statistics, and optimization techniques to identify weed-dense regions and minimize non-target spraying. Incorporating Density-Based Spatial Clustering of Applications with Noise (DBSCAN) with principal component analysis (PCA) and adapting the traveling salesman problem (TSP) algorithms, the system generates optimized UAV trajectories that reduce operational time, chemical usage, and ecological impact. The algorithm demonstrates that the integrated path planning methodology achieves less than 1.5 times the optimal route. Simulation and field datasets show a significant reduction in non-target spraying while maintaining weed knockdown efficacy better than conventional blanket spray approaches. Additionally, the framework addresses practical UAV constraints, including coverage limits, and adaptability for heterogeneous field conditions. 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Motivated by the growing demand for sustainable and resource-efficient farming, the proposed approach leverages unsupervised clustering, spatial statistics, and optimization techniques to identify weed-dense regions and minimize non-target spraying. Incorporating Density-Based Spatial Clustering of Applications with Noise (DBSCAN) with principal component analysis (PCA) and adapting the traveling salesman problem (TSP) algorithms, the system generates optimized UAV trajectories that reduce operational time, chemical usage, and ecological impact. The algorithm demonstrates that the integrated path planning methodology achieves less than 1.5 times the optimal route. Simulation and field datasets show a significant reduction in non-target spraying while maintaining weed knockdown efficacy better than conventional blanket spray approaches. Additionally, the framework addresses practical UAV constraints, including coverage limits, and adaptability for heterogeneous field conditions. This research presents a scalable solution for autonomous agricultural spraying, enabling data-driven farm management, promoting sustainable crop production, and aligning with global trends in precision agriculture and agricultural automation."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Efficient, safe, and sustainable crop protection is a growing challenge in modern agriculture, especially with rising concerns over excessive pesticide use and its impact on human health and the environment. This research explores the use of drones—specifically unmanned aerial vehicles (UAVs)—equipped with advanced path planning and control algorithms to deliver precise spraying only where it is needed. By using sophisticated mathematical techniques to guide drone movement, our system reduces unnecessary chemical application, minimizing potential health risks for farmers, surrounding communities, and consumers. The approach was verified in a realistic software-based simulation environment, demonstrating reliable performance in following a generated trajectory targeting the detected weeds, adjusting to field boundaries, and strictly limiting overspray. Our results show that intelligent drone spraying has the potential to make farming both more productive and safer for people and the planet."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Path planning of agricultural UAVs for combined coverage and spot spraying application"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Kochersberger, Kevin Bruce"],"dc:contributor.committeemember":["Abbott, Amos L.","Komendera, Erik"],"dc:contributor.department":["Mechanical Engineering"],"dc:creator":["Dagadkhair, Rutvik Babasaheb"],"dc:date.accessioned":["2025-10-14T08:00:21Z"],"dc:date.available":["2025-10-14T08:00:21Z"],"dc:date.issued":["2025-10-13"],"dc:description.abstract":["This thesis presents the development of a path planning algorithm for unmanned aerial vehicles (UAVs) to enhance the precision and efficiency of agricultural weed management through combined spot and blanket spraying. 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The approach was verified in a realistic software-based simulation environment, demonstrating reliable performance in following a generated trajectory targeting the detected weeds, adjusting to field boundaries, and strictly limiting overspray. 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