Precision Farming: Geospatial Technologies for Pistachio Orchards

The goal is to integrate geospatial technologies for precision farming. The central theme of the course is the integrated application of Remote Sensing (RS), Geographic Information Systems (GIS), and Information and Communication Technologies (ICT) for optimising agricultural practices, specifically in pistachio farming. The course aims to "Build capacities to use integrated remote sensing and GIS data, and interpret them for Pistachio orchards/trees monitoring." This integration enables a data-driven approach to agriculture, shifting away from traditional, uniform treatments.

Focus on pistachio crop monitoring and management: While the principles are broadly applicable to precision farming, the course has a specific emphasis on monitoring pistachio crop conditions. This includes "canopy volume, weed, pest, etc," and "Monitoring pistachio orchard (soil, trees) and processes (user/location/time-specific)." This specialised focus ensures practical relevance for the VETfarm project in Armenia.

Optimisation of agricultural inputs through management zones (MZ): A key objective is the development and utilisation of MZ. With correctly validated algorithms and parameters, the use of agricultural resources, such as water and sampling, can be optimised in a spatially variable manner using homogeneous production zones, known as Management Zones (MZ). Satellite and GIS data are instrumental in identifying these zones, leading to more efficient resource allocation and reduced waste.

Leveraging cloud-based services and EO data: The course emphasises practical application through the use of cloud-based services/tools for crop monitoring (e.g., WI tools). This approach aims to simplify the process by "removing the complexity of processing large volumes of satellite data." Students will learn to work with "near-real-time EO data," including Sentinel-2 data.

Data enrichment with IoT sensor networks: To enhance the accuracy of monitoring, the course incorporates the use of IoT sensor networks. Enriching EO data through IoT sensor networks, with a particular focus on pistachio crop conditions monitoring and integration of MZ and climate data. This highlights the importance of a multi-source data approach for comprehensive insights.

Practical skills and decision support: The course is designed to be highly practical, with a significant portion dedicated to laboratory hours and exercises. Intended learning outcomes include the ability to "Understand a user-defined location (RoI) and pistachio health issues and then provide a selection of images for RoI for further image analysis." Furthermore, the course focuses on "Computer-assisted information extraction from remotely sensed data for decision support, including case studies based on the needs of the Under Sun (Case Studies to identify pest and water deficiency or surplus, and weed infestations)."

Cost-Effectiveness: A significant benefit highlighted is the "cost-effective basis" of using Remote Sensing Image Analysis in a GIS environment for Precision Farming. This implies that the technologies and methods taught can lead to economic advantages for farmers.