An important area in precision agriculture is related to the efficient use of chemicals\napplied onto fields. Efforts have been made to diminish their use, aiming at cost reduction and\nfewer chemical residues in the final agricultural products. The use of unmanned aerial vehicles\n(UAVs) presents itself as an attractive and cheap alternative for spraying pesticides and fertilizers\ncompared to conventional mass spraying performed by ordinary manned aircraft. Besides being\ncheaper than manned aircraft, small UAVs are capable of performing fine-grained instead of the\nmass spraying. Observing this improved method, this paper reports the design of an embedded\nreal-time UAV spraying control system supported by onboard image processing. The proposal uses\na normalized difference vegetation index (NDVI) algorithm to detect the exact locations in which\nthe chemicals are needed. Using this information, the automated spraying control system performs\npunctual applications while the UAV navigates over the crops. The system architecture is designed\nto run on low-cost hardware, which demands an efficient NDVI algorithm. The experiments were\nconducted using Raspberry Pi 3 as the embedded hardware. First, experiments in a laboratory were\nconducted in which the algorithm was proved to be correct and efficient. Then, field tests in real\nconditions were conducted for validation purposes. These validation tests were performed in an\nagronomic research station with the Raspberry hardware integrated into a UAV flying over a field of\ncrops. The average CPU usage was about 20% while memory consumption was about 70 MB for high\ndefinition images, with 4% CPU usage and 20.3 MB RAM being observed for low-resolution images.\nThe average current measured to execute the proposed algorithm was 0.11 A. The obtained results\nprove that the proposed solution is efficient in terms of processing and energy consumption when\nused in embedded hardware and provides measurements which are coherent with the commercial\nGreenSeeker equipment.
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