International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Precision agriculture is considered one of the key solutions to satisfy world food demand by 2050. To achieve precision at the smallest farming unit, data collected, processed, and analysed to produce fit-for-use knowledge must be visible, sharable, and accessible among all stakeholders. Cloud computing technology on the foreshore has penetrated all segments of everyday life, not sparing the agriculture segment, making visible the nondirectional flow of information in real time. Cloud computing generally provides scalable infrastructure, powerful storage, data-sharing facilities, low operational costs, and powerful analytics, metering AI/ML model development and inference more affordable and enabling artificial intelligence and machine learning development and inference at regional scales. Its challenges include single-point failure, low-quality service, latency, and resource dependency. Conclusively, precision agriculture at the farm and field levels can also benefit from clustering agriculture stakeholders’ data on the cloud and making the data accessible to eminent researchers and agriculture domains using various algorithms to develop AI/ML models. The planned architecture helps shed light on the logical flow of information from the sensor to decision representation. It includes key functional modules, components required for data ingestion, integration towards data lakes, model training nested in the cloud, deployment in different environments for inference, and governing data with respect to modelling, privacy, and security of stakeholders’ data in the cloud.
To satisfy world food demand by 2050, precision agriculture is considered one of the key solutions. Precision requires collecting, processing, and analysing data to produce fit-for-use knowledge that is visible, sharable, and accessible among all stakeholders. Cloud-computing technology on the foreshore has penetrated all segments of everyday life, not sparing agriculture, and enabling a non-directional flow of real-time information. Cloud computing generally provides scalable infrastructure, powerful storage, data-sharing facilities, low operational costs, and powerful analytics, making artificial intelligence and machine learning development and inference at regional scales more affordable. The technology’s challenges include single-point failure, low-quality service, latency, and resource dependency. Precision agriculture at the farm and field levels can benefit from clustering agriculture stakeholders’ data on the cloud and making the data accessible to eminent researchers and agriculture domains.

Keywords: Precision Agriculture, Cloud Computing In Agriculture, Smart Farming Systems, Agricultural Data Analytics, IoT Sensors In Farming, Real-Time Farm Data, Agricultural Data Sharing, Scalable Cloud Infrastructure, AI And ML In Agriculture, Farm-Level Decision Support, Data Ingestion Pipelines, Agricultural Data Lakes, Model Training And Inference, Edge–Cloud Agriculture Architectures, Food Security 2050, Stakeholder Data Accessibility, Agricultural Data Governance, Privacy And Security In Agri-Data, Latency And Reliability Challenges, Digital Agriculture Ecosystems.

Downloads: | DOI: 10.17148/IJARCCE.2024.131270