Tracking and Predicting Productions in Agricultural Processes: Applications and Experiences

The quality and traceability of agricoltural and food products (indicated as agri-food) represents an important task for industries to focus on environments and wellness targets. In the context of milk and vegetable production processes, it is no possible to monitor and control animals behaviour, environmental conditions, and overall quality affecting these productions. Accurate and explainable predictions of quantities, as well as food properties qualities, is relevant for marketing and planning action in agri-food companies thus to in obtaining more efficient higher-quality productions and contribute to citizens wellness.We here report examples and experiences of machine learning algorithms application to evaluate and predict quantity and frequency of production in an large south of Italy farm. Data are extracted from a tracking system storing all production phases, i.e.: (i) from fruits plants to storage, cold maintaining and transportation, and (ii) cows management, fresh milk analysis and packaging. The here proposed experience contributes to evaluate and predict quantity and frequency of production, aiming to support farms in product planning and production phases.