Smart Pasture Management for Optimizing Grazing Capacity and Herbage Production

Grassland and pasture management increasingly requires timely and reliable decision support to address changing environmental conditions, optimize grazing capacity, and improve herbage production. Although digital technologies such as the Internet of Things (IoT), Cloud Computing, Digital Twins, Artificial Intelligence (AI) and Machine Learning (ML) have been widely adopted, they are often implemented as isolated solutions rather than as an integrated management framework. This paper proposes a cloud-based smart grazing framework that combines field monitoring, biomass forecasting, digital twin monitoring, and stocking optimization within a unified architecture. The framework includes two interconnected algorithms: the first supports the operational grazing management cycle through IoT sensing, biomass forecasting, digital twin monitoring, and stocking optimization, while the second enables secure ML training and model updating for biomass prediction, livestock health assessment, and grazing behavior analysis. To ensure data integrity with low computational overhead, the framework employs SHA-256 hash-based verification rather than a full blockchain implementation. The proposed architecture provides a practical approach for integrating monitoring, prediction, and secure data management to support sustainable grazing management.