Digital twin of rice as a decision-making service for precise farming, based on environmental datasets from the fields

Abstract

In this paper a ready-to-use software component, which simulates real state of rice crop in the field and called “Digital Twin of rice” (DT), is studied. DT uses ontology-based knowledge base of plant cultivation to execute the rules of plant growth. The software provides real-time data collection from the fields and distributed decision making to find the optimum solution in planning process of rice growth stages. Rice DT is developed as an autonomous service and can be integrated to any existing digital agricultural platform. A pilot integration with cyber-physical system (CPS) for precise farming is described in the paper. The CPS has a number of services to provide digital transformation in plant cultivation enterprises and big farms. The system performs adaptive scheduling of resources, such as fertilizers, protection agents, vehicles, personnel and finance. Results of DT implementation shows adequate decision-making of the service compared to experiments on the pilot farms. So, DT of plant could be a next step in digital transformation of agriculture, providing improvement of ROI from precision farming, automate decision-making processes for farmers and service companies and make their business smarter, more flexible, and more cost-efficient, providing better productivity of plant cultivation and sustainability of agriculture under global climate changes.