IoT-Driven Machine Learning for Precision Viticulture Optimization

Abstract

Precision Agriculture (PA), also known as Smart Farming, has emerged as an innovative solution to address contemporary challenges in agricultural sustainability. A particular sector within PA, Precision Viticulture (PV), is specifically tailored for vineyards. The advent of the Internet of Things (IoT) has facilitated the acquisition of higher-resolution meteorological and soil data obtained through in situ sensing. The integration of Machine Learning (ML) with IoT-enabled farm machinery stands at the forefront of the forthcoming agricultural revolution. This data allows ML-based forecasting as an alternative to conventional approaches, providing agronomists with predictive tools essential for improved land productivity and crop quality. This study conducts a thorough examination of vineyards with a specific focus on three key aspects of PV: mitigating frost damage, analyzing soil moisture levels, and addressing grapevine diseases. In this context, several ML-based models are proposed in a real-world scenario involving a vineyard located in southern Italy. The test results affirm the feasibility and efficacy of the ML models, demonstrating their potential to revolutionize vineyard management and contribute to sustainable agricultural practices.