End-to-end precision agriculture UAV-based functionalities tailored to field characteristicsEnd-to-end precision agriculture UAV-based functionalities tailored to field characteristics Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2025-07-312023enThis paper presents a novel, low-cost, user-friendly Precision Agriculture platform that attempts to alleviate the drawbacks of limited battery life by carefully designing missions tailored to each field’s specific, time-changing characteristics. The proposed system is capable of designing coverage missions for any type of UAV, integrating field characteristics into the resulting trajectory, such as irregular field shape and obstacles. The collected images are automatically processed to create detailed orthomosaics of the field and extract the corresponding vegetation indices. A novel mechanism is then introduced that automatically extracts possible problematic areas of the field and subsequently designs a follow-up UAV mission to acquire extra information on these regions. The toolchain is finished by using a deep learning module that was made just for finding weeds in the close-examination flight. For the development of such a deep-learning module, a new weed dataset from the UAV’s perspective, which is publicly available for download, was collected and annotated. All the above functionalities are enclosed in an open-source, end-to-end platform, named Cognitional Operations of micro Flying vehicles (CoFly). The effectiveness of the proposed system was tested and validated with extensive experimentation in agricultural fields with cotton in Larissa, Greece during two different crop sessions.This research has been financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH - CREATE - INNOVATE (T1EDK-00636).http://creativecommons.org/licenses/by/4.0/Journal of Intelligent and Robotic Systems1072Raptis_et_al_J. Intell. Robot. Syst._107(2)_2023.pdfChania [Greece]Library of TUC2025-07-31application/pdf7.7 MBfree Raptis Emmanuel K. Krestenitis Marios Egglezos Konstantinos Kypris Orfeas Ioannidis Konstantinos Doitsidis Eleftherios Δοιτσιδης Ελευθεριος Kapoutsis Athanasios Ch. Vrochidis Stefanos Kompatsiaris Ioannis Kosmatopoulos Ilias Κοσματοπουλος Ηλιας Springer Precision agriculture UAVs Coverage Remote sensing Site-specific inspection Convolutional neural networks Weed detection