This abstract discusses smart agriculture techniques used for the part of the Deep Farm Erasmus+ project applied at İzmir, Turkey by the Turkish coordinator Yasar University, focusing on olive trees and olive products. The overall coordinator of Deep Farm project is ESTIA Institute of Technology of France. The aim is to improve olive farming practices by enabling AI supported decision-making through the analysis of real-time and historical data. Advanced AI technologies on the software side and modern equipment like in-ground sensors and drones are used to enhance olive production performance.

The methodology employed in the Deep Farm project case study for olive cultivation was designed to integrate advanced technological solutions with practical agricultural needs. This approach has several key stages, beginning with comprehensive data collection from diverse sources, including real-time field data and historical records, to form a robust dataset. The system architecture was developed to facilitate seamless data flow and processing, enabling the application of advanced machine learning models on the collected data, focusing on the creation and optimization of AI-driven tools for disease detection and weather prediction, specifically tailored for olive farming.

Key achievements so far include the deployment of AI-driven models for olive farming. A You Only Look Once (YOLO) deep learning model-based disease detection system has been implemented for olive trees. Additionally, a Hybrid Gated Recurrent Units (GRU) model has been developed for dynamic weather prediction. Integration of field data and historical weather records is successfully done which further enhances the system’s predictive capabilities.

As part of the project, a summer school was organized in İzmir in September 2024 to promote the application of AI in agriculture. The program included face to face training on the fundamentals of machine learning, examples of AI tools used in agriculture across various countries, and the development of AI-based systems for weather prediction and disease detection. Lecturers and students from the Caribbean (Haiti and Dominican Republic) and African (Ivory Coast and Madagascar) countries participating in the Deep Farm project attended the summer school. To ensure accessibility, face-to-face sessions organized during the summer school were also recorded and shared with the students who could not attend the face-to-face sessions.

Students from the Faculty of Agricultural Sciences and Technologies and Faculty of Engineering from Yasar University also attended the summer school. They were provided with theoretical information about the olive tree yearly life cycle, and were assigned three olive trees at the Izmir Olive Research Institute, close to the Yasar University campus. Students are given guidelines and forms for olive tree observation. They are performing regular olive tree inspections and are taking close-up pictures of olive tree leaves to be used in disease detection.

At the end of the olive case study in December 2025, we believe the results will show that continuous and systematic observation of crops, dynamic weather prediction systems, use of modern farming tools, and proper use of AI techniques and tools for early detection of diseases will help sustainable and innovative agricultural practices in olive production.

Keywords: Agriculture, Artificial Intelligence, Education.