An AI-Driven Architecture for Precision Agriculture: IoT, Machine Learning, and Digital Twin Integration for Sustainable Crop Protection

General efficient crop protection in olive orchards is challenged by the need for precise agrochemical application minimizing environmental impact while ensuring effective spray coverage. Conventional approaches often lead to poor spray distribution, heavy off-target losses and a lack of real-time adaptability in variable field conditions. Such findings imply the need for an approach or system that can adjust application parameters dynamically to optimize both effectiveness and sustainability.This paper introduces an advanced AI-driven architecture designed to meet these challenges by integrating Internet of Things (IoT), machine learning, and digital twin technologies. The core of this approach is the development of a Digital Tree, a virtual model of olive trees that accurately simulates and predicts spray distribution and interactions with environmental variables. IoT sensors in the field collect real-time data on spray behavior and field conditions, which machine learning algorithms then process to refine application parameters dynamically. By enabling data-driven, adaptive decision-making, the digital twin supports optimal spray distribution, reduces off-target impact, and enhances environmental sustainability. This integrated solution offers a scalable and replicable methodology for smart agriculture, advancing precision and sustainability in complex agricultural environments.