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Unmanned Aerial Vehicles (UAVs) consist of emerging technologies that have the potential to be used gradually in various sectors providing a wide range of applications. In agricultural tasks, the UAV-based solutions are supplanting the labor and time-intensive traditional crop management practices. In this direction, this work proposes an automated framework for efficient data collection in crops employing autonomous path planning operational modes. The first method assures an optimal and collision-free path route for scanning the under examination area. The collected data from the oversight perspective are used for orthomocaic creation and subsequently, vegetation indices are extracted to assess the health levels of crops. The second operational mode is considered as an inspection extension for further on-site enriched information collection, performing fixed radius cycles around the central points of interest. A real-world weed detection application is performed verifying the acquired information using both operational modes. The weed detection performance has been evaluated utilizing a well-known Convolutional Neural Network (CNN), named Feature Pyramid Network (FPN), providing sufficient results in terms of Intersection over Union (IoU).
Raptis et al. (Thu,) studied this question.