Remote Sensing and High-Resolution 3D Modelling of Crop Conditions for Aiding in Precision Agriculture
Results produced in H2020-AgriBIT project have been presented, including three key technologies developed by RFSAT Limited: Centimetre accuracy, economic GNSS receiver for enabling provision of Precision Agriculture services at previously unachievable spatial resolution. High-resolution multispectral imaging enabling production of 3D crop models for determining health conditions and modelling 3D canopies to resolution exceeding 1cm/pixel for monitoring growth and more accurately predicting expected harvests. Analytics of info produced in (2) with aid of advanced AI techniques for automatically determining and precisely locating cases of degraded crop health incl. water and nutrient depletion, bacterial and pest infestations for speeding-up localized response, reducing human effort, costs of equipment, water for irrigation, fertilizers and pharmaceuticals. Sensing technologies from RFSAT, ranging from custom-built calibrated high-accuracy environmental sensors, public and private Open Data repositories, including national and international authorities, provided high spatial and temporal database of info based on which advanced services developed in AgriBIT by other partners, could be provided, e.g. Crop Growth Monitoring Crop yield estimation and seasonal yield prediction Irrigation Scheduling Tillage Scheduling Pest and Disease Early Warning Prescription mapping Climate and weather services for farmers etc. Results were evaluated at three pilot sites, in: Greece showing benefits for peach orchards, from the capability to overcome limitation of Earth Observation and UAV due to tree canopy shapes Portugal aimed at precise management of diseases, speeding up detection during the growth and enabling corrective actions earlier than without use of GNSS services, e.g. before harvest Italy for 200 ha of vineyards, focusing on ability to improve automatic guidance, decreasing water consumption and interoperability with 3rd-party platforms.
Institutions
- Realistforeningen (NO)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-06-17
- DOI
- https://doi.org/10.5281/zenodo.15685627
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 0.00