The Open Call (OC) will select 9 new Sustainable Innovation Pilots (SIPs) of the same structure as the 5 core SIPs. The OC will focus on countries, crops, overall conditions, production processes and/or technologies not already covered by project SIPs, with the expectation that they will benefit from the most recent developments and innovations in the area. A set of 5 SIPs has been defined, including a variety of solutions, sectors, countries and underlying conditions: a) Agri-food sectors, b) Countries; c) Real-life needs; and d) Technologies.

The following types of applications/solutions (but not limited to) are expected to be funded:

• Crop Pest and/ or Disease Detection: ADSs with innovative, cost-effective, and energy-efficient opensource software and/or hardware solutions for accurately detecting pests and diseases in crops, using AI-based analytics, ML models and sensor technologies.
• Animal Behaviour Monitoring: ADSs to monitor and identify behavioral patterns of animals in different environments (e.g. open areas or stables), addressing challenges in both ruminant and non-ruminant livestock management
• Animal Health Monitoring: ADSs to monitor the health of animals in various environments using realtime data, AI, and ML models to detect signs of illness and ensure timely interventions.
• Animal Disease Detection: Implementing ADSs for the early detection of diseases in both ruminant and non-ruminant livestock, utilizing AI and ML models to analyze data and image patterns for accurate diagnosis.
• Animal Welfare Monitoring: ADSs to enhance animal welfare by using image data and or other data types, AI, and ML models to monitor and assess the well-being of livestock, ensuring humane and optimal living conditions.
• Smart Irrigation Management: ADSs to optimize irrigation and water consumption, using real-time soil moisture and/ or weather data, AI-based analytics, ML models to improve water use efficiency and support sustainable farming practices.
• Smart Fertilization Management: ADSs to optimize fertilization practices, using soil nutrient data and/ or Image data which through AI-based analytics and ML models enhance nutrient use efficiency and support sustainable farming practices
• Crop Growth and Health Monitoring: Developing sensor-data-driven ADSs for real-time monitoring of crop growth and health, soil quality, and weather patterns, contributing to better crop production management.
• Soil Health, Soil Quality and Weather Analysis: ADSs to analyze soil health, soil quality and weather patterns, using real-time data, AI, and ML models to process and interpret data from soil maps, insolation, crop types, growth conditions, precipitation accumulation, and weather forecasts, enhancing decision-making in crop production.
• Farm Management and Decision Support: ADSs that integrate real-time data across farming operations, using AI and ML models to enhance decision-making, improve operational efficiency, and boost overall farm productivity.
• Farm automations and robotics: Open-source cloud/edge based ADSs performing automated weeding, harvesting, pruning, or spraying.
• Biodiversity monitoring: Solutions capable of automated identification of species (birds, insects, mammals, etc.), counting of number, and categorisation (e.g. beneficial and non-beneficial insects), considering agriculture and biodiversity aspects.
• Agroecology, agroforestry, organic and regenerative agriculture: Solutions capable of enhancing the management of complex agronomic practices, such as multicropping, agroforestry, regenerative land management or similar where data-driven solutions enable scalability or semi-automation.
• Supply chain management: First mile traceability and automation, e.g. open source and cloud edge computing (including hardware) for monitoring the process from harvesting to packaging/processing operations in support of agri-food traceability.

Submissions will ONLY be done via the OCH platform. A full list of applicants will be drafted containing their basic information for statistical purposes and clarity (which will be also shared with the EC for transparency).