Digital Twin Technology

A digital platform that integrates people with the machines and data they use is the first step toward a digital representation of any industry. In agriculture, this means storing and connecting information about stakeholders, LOTS of data, and financial analytics.

There’s simply too much data to create useful insights based on what human beings can enter manually. Automating analytics through Internet-of-Things (IoT) and other machine-driven mechanisms can enable us to gather orders of magnitude more data from sensors and other sources without human intervention.

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 are to simulate outcomes throughout a growing season, we must know everything we can about a potential crop so we can tweak those inputs to identify our best bet at a good harvest

Last, we need 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. These stresses are consistent drags on agricultural output and tend to occur regularly in the same places. Managing them requires that we identify and measure them.

With this Digital Twin in hand, we can answer those “How might we” questions up above. We can simulate, plan, analyze, and improve the way we grow crops. We can maximize yields, reduce stresses on water supplies and soil quality, and help make farming a sustainable practice throughout the world.

Farming is heavily driven by discrete workflows. Carefully defining the steps in this complex workflow enables us to categorize actions and their place in the value chain from farm to consumer.

In a manufacturing setting, one might look at raw materials as the productive asset — we take this stuff over there, assemble it into something new, and turn it into something worth more than the sum of its raw materials. In agriculture, that productive asset is the soil. It’s the constant in land-based agriculture (hydroponics represent a slight variation here, of course). An agricultural Digital Twin requires us to measure and understand everything we can about the content and capacity of the soil in which crops grow.

The input we can’t control, only predict. Is there such a thing as too much water for a given crop? Not enough? Can we make our water use more efficient by planning around a particularly wet or dry forecast? How will the effects of anthropogenic climate change affect our long-term outlook? Many companies are making massive investments in weather modeling and prediction using Artificial Intelligence (AI) and other technologies. These predictive models can be leveraged as part of our agricultural Digital Twin