Speeding up UAV-based crop variability assessment through a data fusion approach using spatial interpolation for site-specific management

• DOI: 10.1016/j.atech.2024.100488
• Publication Date: 2024-06-15T03:58:45Z
• Authors: Sergio Vélez; Mar Ariza-Sentís; Marko Panić; Bojana Ivošević; Dimitrije Stefanović; Jere Kaivosoja; João Valente
• Keywords: Interpolation; Sensor Fusion

Innovations in precision agriculture enhance complex tasks, reduce environmental impact, and increase food production cost efficiency. One of the main challenges is ensuring rapid information availability for autonomous vehicles standardizing processes across platforms to maximize interoperability. The lack drone technology standardisation, communication barriers, high costs, post-processing requirements sometimes hinder their widespread use agriculture. This research introduces a standardized data fusion framework creating real-time spatial variability maps using images from different Unmanned Aerial Vehicles (UAVs) Site-Specific Crop Management (SSM). Two interpolation methods were used (Inverse Distance Weight, IDW, Triangulated Irregular Networks, TIN), selected computational efficiency input flexibility. proposed can UAV image sources offers versatility, speed, efficiency, consuming up 98 % less time, energy, computing than standard photogrammetry techniques, providing field information, allowing edge incorporation into acquisition phase. Experiments conducted Spain, Serbia, Finland 2022 under H2020 FlexiGroBots project demonstrated strong correlation between results this method those techniques (up r = 0.93). In addition, with Sentinel 2 satellite was as that obtained photogrammetry-based orthomosaics 0.8). approach could support irrigation leak detection, soil parameter estimation, weed management, integration