The ORCHARDSCAN project aims to develop an innovative method for monitoring apple orchards using remote sensing and ground-based measurements to enable the early detection of harmful organisms, particularly diseases and pests affecting tree health as well as crop yield and quality. The project combines data acquired from multiple Earth observation sources with advanced data analysis techniques and artificial intelligence.
The project is based on a multi-level remote sensing monitoring approach that includes ground measurements, unmanned aerial vehicle (UAV) imagery, and satellite data, including imagery from the PRISMA, Sentinel-2, and Landsat missions. In addition, meteorological data and the PROSAIL radiative transfer model will be used to improve the interpretation of processes occurring within tree canopies. The integration of multiple information sources will make it possible to detect the first signs of pest and disease occurrence even before visible symptoms appear.
One of the project’s main objectives is the development of an advanced model based on Fusion Network neural architectures, enabling the effective integration of data with different spatial, spectral, and temporal characteristics. The model will be trained using the results of field studies, including hyperspectral measurements, plant fluorescence, leaf area index (LAI), and leaf temperature measurements. This approach will make it possible to identify the set of indicators that most effectively detect diseases and pests in apple orchards.
The project will deliver a demonstrator enabling the automated processing and analysis of multi-source remote sensing data, as well as a web-based graphical user interface tailored to the needs of end users, particularly orchard growers. The system will support decision-making in orchard protection by enabling faster threat detection and more precise planning of plant protection measures. The developed solution will contribute to reducing losses in fruit production, lowering the use of plant protection products, and advancing modern precision agriculture.
The project outcomes will include a comprehensive methodology for detecting pests and diseases in apple orchards, an artificial intelligence model integrating data from multiple sources, a monitoring system demonstrator, and a web platform enabling the practical use of the developed tools by end users.
Project Coordinator: Institute of Geodesy and Cartography
Partners:
- Institute of Horticulture – National Research Institute
- Maria Curie-Skłodowska University
- Orbify Poland Sp. z o.o.
- Scuola di Ingegneria Aerospaziale – Università di Roma “La Sapienza” (Italy)
Project Duration: 2025–2027
Funding: National Centre for Research and Development (NCBR) – INNOGLOBO Programme (3rd Call).