Federated learning based reference evapotranspiration estimation for distributed crop fields.

ABSTRACT: Water resource management and sustainable agriculture rely heavily on accurate Reference Evapotranspiration (ETo). Efforts have been made to simplify the (ETo) estimation using machine learning models. The existing approaches are limited to a single specific area. There is a need for ETo estimations of multiple locations with diverse weather conditions. The study intends to propose ETo estimation of multiple locations with distinct weather conditions using a federated learning approach. Traditional centralized approaches require aggregating all data in one place, which can be problematic due to privacy concerns and data transfer limitations. However, federated learning trains models locally and combines the knowledge, resulting in more generalized ETo estimates across different regions. Th

SUBMITTER: Tausif M 

PROVIDER: S-EPMC11798497 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature