Abstract
The growing adoption of digital technologies in agriculture is opening new opportunities for improving productivity, sustainability and resource efficiency through data-driven decision making. This paper presents the integrated precision-agriculture framework developed by RFSAT Limited within the H2020 AgriBIT project and currently being further enhanced through the ENACT project. The framework combines Internet-of-Things (IoT) sensing, centimetre-accuracy Global Navigation Satellite System (GNSS) positioning, high-resolution multispectral remote sensing and Artificial Intelligence (AI) analytics into a single smart-farming solution. A key enabler is a low-cost, centimetre-accurate GNSS receiver developed by RFSAT, which allows precise georeferencing of aerial and ground observations and, in turn, the generation of high-resolution multispectral 3D models of crop canopies with spatial resolution exceeding 1 cm/pixel. These digital representations support detailed monitoring of crop growth, canopy development, biomass distribution and plant health throughout the season. Fused with an IoT monitoring infrastructure of environmental and microclimatic sensors, the georeferenced remote-sensing products form a comprehensive digital twin of the agricultural environment, over which machine-learning techniques automatically identify and localise crop anomalies including water stress, nutrient deficiencies, disease outbreaks and pest infestations. The technologies have been validated at operational pilot sites across Greece, Portugal and Italy, spanning orchards, vineyards and other high-value crops, where the combination of centimetre positioning, multispectral 3D modelling, IoT monitoring and AI analytics substantially improved monitoring accuracy, reduced input consumption and enhanced management decisions.