Digital twin and AI optimization for water allocation to improve economic outcomes and reduce irrigation in maize production
A digital twin and AI approach that optimizes water allocation and irrigation strategies for maize. It combines the DSSAT CERES maize crop model with an economic model to simulate how different water strategies affect yield and economic outcomes. Reinforcement learning is used to learn actions that maximize economic benefits while minimizing water use, supporting sustainable agricultural decision-making across variable weather, soil, and water availability.
Overview
The solution leverages digital twins and AI to optimize water allocation and irrigation strategies, aiming to improve yield and economic benefits in the Corn Belt region. By integrating the DSSAT CERES maize model with an economic model, it simulates the impact of different water allocation strategies on economic outcomes. Reinforcement learning is used to identify optimal actions that maximize economic benefits while minimizing water usage, supporting sustainable agricultural practices.
Technical specifications
The core of this solution is a digital twin model combining:
- DSSAT CERES maize model: Uses weather and soil data to predict crop yield.
- Economic model: Incorporates commodity pricing, insurance, subsidy, and incentive policies to simulate economic impacts.
- Reinforcement learning framework: Trains models to identify optimal water allocation and irrigation strategies.
This system is designed to adapt to regional conditions, accounting for variable weather, soil, and water access to support robustness and scalability across different farm setups.
Technology readiness level
Currently at Technology Readiness Level 6, the solution has been validated through simulations and small-scale crop trials. Future plans include scaling up validation through additional agricultural stakeholder engagement and comparing results with historical economic performance to improve accuracy and reliability before broader implementation.
About Vineland Research and Innovation Centre
Vineland Research and Innovation Centre is an independent, not-for-profit horticultural research organization in Vineland Station, Ontario, with a team of 100+ people focused on translating science into market-ready solutions. Its integrated campus combines pre-commercial greenhouses, advanced pathology and soil/substrate labs, a prototyping hangar for robotics and sensors, dedicated sensory and food technology suites, and field-scale research farms. A research-park setting with co-located government, academic and industry tenants enables on-site collaboration, testing and demonstrations. Vineland is funded in part through the Sustainable Canadian Agricultural Partnership, with support from Agriculture and Agri-Food Canada and the Government of Ontario. Commercialization is supported via defined licensing pathways and an active intellectual property portfolio.
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