the uses of digital twins in agriculture

The promises of digital twins in Ag is that they are goint to allow farmers and their crop consultants to simulate, plan, analyze, and improve crop growth way ahead of time — maximizing yields and making farming more sustainable.
 
What is a digital twin?
When we assemble all Ag-relevant digital technologies into a cohesive platform, we end up with a digital representation of the entire agricultural effort: physical assets, processes, systems, resources, finance, in short, everything. In turn, this representation allows to simulate, plan, analyze, and improve agricultural processes at a previously unimagined scale – that’s what digital twins do. 
 
Digital Twins aren’t new, but in definitely in agriculture they’re most certainly a convincing approach
 

• Integrated Digital Platform: a digital platform that integrates people with ther machines and data they use is a good first step towards a digital representation, and in agriculture, this means storing and connecting information about stakeholders, LOTS of data, and financial analytics.
• Workflow Engine: Farming is heavily driven by discrete workflows and precisely defining them helps industry players to categorize actions and their place in the value chain from farm to consumer.
• Analytics Automation: There’s too much data to create predictive insights and analytics using connected sensors and other machine-driven mechanisms enable more data gathering from sensors and other sources without human intervention – in turn modeling (whether this is actual data or synthetic datasets) bridges the gap between a very slow pace of agriculture and the real time aspect of technology.
• Soil: The Productive Asset: in agriculture, the productive asset is the soil. It’s the constant in land-based agriculture and understanding as much as possible in the content and capacity of the soil in which crops grow help refine digital twin modeling.
• Inputs Digital Modeling: similar to the description for soil as the productive assets, the seeds and crops that require that soil must be described — what is their expected yield? How much fertilizer is required? Sunlight? Water? If we going to simulate outcomes throughout a growing season, we must know everything we can about a potential crop to maximize harvest outcomes.
• Weather Prediction: hello randomness here? Is there such a thing as too much water for a given crop? Not enough? Can water use be made more efficient by planning around a particularly wet or dry forecast? How will the effects of climate change affect the long-term outlook? Predictive weather modeling can be also leveraged as part of the agricultural Digital Twin.
• Persistent Stress Identification: last, the ability to identify where and how the agricultural system’s resources are stressed, whether by invasive plants and animals, soil quality, pollution, or other factors. Managing them requires that to identify, measure them for better modeling over time.

 
In conclusion, digital twin modeling in agriculture helps to simulate, plan, analyze, and improve the way we grow crops to maximize yields, reduce stresses on water supplies and soil quality, and help make farming a sustainable practice. 

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​My name's phil mora and I blog about the things I love fitness, hacking work, tech and anything holistic. 
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