End-to-end Precision Agriculture UAV-Based Functionalities Tailored to Field Characteristics

Abstract

This 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.

Advancements of UAV and Deep Learning Technologies for Weed Management in Farmland

“…Eide et al [109] used two DJI M600 Pro drones as data collection equipment, equipped with Zenmuse XT2 RGB/Thermal camera and Micasense Red-Edge MX dual camera system, respectively. In order to obtain images with better resolution, data were collected at 8 m and 10 m [110]. They believe that the selection of suitable drones and vision sensors and the planning of flight paths are very meaningful for the quality of the data.…”

Design of hexacopter and finite element analysis for material selection

“…These UAV systems can be used for a wide range of agricultural applications that include pesticides spraying and aerial seeding (Sai et al, 2020), estimation of leaf area index (LAI) (Raj et al, 2020), leaf disease detection (Tetila et al, 2019), tree height estimation (Sadhasivam et al, 2020), real-time monitoring and mapping of agricultural land (Meivel and Maheswari, 2021). Further extending the scope of aerial mapping of farmlands, a study describes an innovative, low-cost smart agricultural solution that seeks to overcome the limitations of a short flight time of the UAV by planning missions adapted to each field's unique parameters (Raptis et al, 2023). Basso and Pignaton de Freitas (2020) proposes an embedded guidance system for UAVs that includes a crop row recognition algorithm.…”

“…, 2020), real-time monitoring and mapping of agricultural land (Meivel and Maheswari, 2021). Further extending the scope of aerial mapping of farmlands, a study describes an innovative, low-cost smart agricultural solution that seeks to overcome the limitations of a short flight time of the UAV by planning missions adapted to each field's unique parameters (Raptis et al. , 2023).…”

Improve Time and Energy Efficiency in Multi-UAV Coverage Swarms by Optimizing theTake-off/Landing positions

“…Nowadays, a wide range of enterprise domains take advantage of the remote sensing capabilities offered by Unmanned Air Vehicles (UAVs) to collect data for various purposes. The scope of applications that utilize UAVs for data collection includes precision agriculture [1][2][3], infrastructure inspection [4,5], exploration [6,7] and monitoring [8,9]. To efficiently collect data in an automated way, Coverage Path Planning (CPP) methodologies [10,11] are commonly utilized to calculate paths for the UAVs to cover a user-defined Region of Interest (ROI) completely.…”