AISE

The AISE project, spanning 36 months, aims to revolutionize plant health through advanced information and intelligence systems, employing AI for Solanaceae Integrated Pest Management (IPM). This initiative integrates robust hardware and software to deploy Deep Learning (DL) models, facilitating accurate detection of insect and fungi symptoms on plants. The smart systems focus on a) Phytophthora (Ph) and Alternaria solani (As) infestations in tomatoes, utilizing an AI-based smartphone hyperspectral module for on-site analysis and AI powered mobile apps for citizen science, and b) Tuta absoluta (TA) infestations, employing an AI-based robotic trap and mobile applications.

Interconnected in a systemic approach, these AI-based systems form a Decision Support System (DSS), ensuring data verification and encryption with cybersecurity measures. Rich data visualizations based on Machine Learning (ML) algorithms aid in automated decision-making for IPM, considering weather and climate data. These systems integrate with the existing IoT-based Agro-toolbox and auto-navigated Agro-Robotic systems for real-time inspection and identification of pests.

Testing and optimization occur in controlled environments, followed by field demonstrations in Greek open fields and Spanish greenhouses. AISE adheres to a multi-actor approach, addressing real-world challenges and aligning with Green Deal targets for a 50% reduction in pesticide use by 2030. The project establishes an Industrial Advisory Board (IAB) with over 5 stakeholders from Farm to Fork (F2F) to foster synergies supporting sustainability, resilience, and citizen science activities.

Consortium

Center for Research and Technology Hellas
Council for Agricultural Research and Economics (CREA)
Jardinería y viveros La Noria, S.L
UNIVERSITY OF THESSALY