Designing Effective Smart Farm Information Processing Platforms: An AHP-Guided Methodology

Abstract

As the global population expands, the demand for increased food production necessitates a corresponding rise in agricultural productivity. Smart farming presents a transformative solution by leveraging advanced technologies such as the Internet of Things (IoT), Cloud Computing, and Artificial Intelligence to optimize resource management and enhance productivity. These technologies enable the collection of vast amounts of data from diverse sources like weather stations, sensors, cameras, and smartphones. This data is crucial for decision support systems that provide actionable insights to improve farm management. However, smart farming faces significant challenges in managing and utilizing the massive volumes of data generated. A primary hurdle is the heterogeneity of agricultural data in terms of format, structure, and semantics, which complicates data integration and hinders effective communication between different systems. The lack of standardized protocols limits the seamless exchange of information among devices, software, and services within the smart farming ecosystem. These obstacles impede data sharing and collaborative service delivery, ultimately hindering the adoption and success of smart farming platforms. To address these challenges, this study employs the Analytic Hierarchy Process (AHP) to systematically evaluate and prioritize the key requirements for designing effective smart farming platforms. This study provides a structured framework for prioritizing essential requirements to enhance smart farming platforms. Addressing these prioritized requirements will facilitate the integration of diverse data sources, improve interoperability, and promote effective data-sharing practices. Ultimately, this will lead to increased productivity and efficiency in modern agricultural systems, meeting the growing global demand for food production.