Data sharing key for AI in agriculture

Companies that develop Artificial Intelligence (AI) depend heavily on data from farmers for their machine learning algorithms. Tremendous amounts of data are needed to train these systems. “Otherwise a companies product could be less accurate than the system of a competitor with more data. This also shows why some people have been describing data as a new form of currency,” states the Agricultural Data Coalition (ADC) in an article on their website.

Monetary value

Data gathered and provided by farmers is very valuable and important to train algorithms. At the same time it’s difficult to quantify the monetary value of any particular set of data fed into the system. “Individually the data records have little value, their true significance comes though when combined with many other records,” says the ADC.

_{Text continues underneath image}

Data gathered and provided by farmers is very valuable and important to train algorithms. At the same time it’s difficult to quantify the monetary value of any particular set of data fed into the system. – Photo: Jan Willem Schouten

Therefore, the Agricultural Data Coalition says, “it is important all parties know what is going on, who needs what data, what they are doing with it, and what benefits each party receives from sharing it. This is an exciting time to be involved with agriculture and AI will undoubtedly have a substantial impact on how farming is done for years to come.”

While Artificial Intelligence holds great promises for use in agriculture, these products have yet to take over the market. Why? According to the ADC, one hurdle is having enough data available. “If you are trying to match up all the different variables for a field you need a lot of different combinations of soil types, weather, seed, fertility, planning dates, etc.”

_{Text continues underneath image}

While Artificial Intelligence holds great promises for use in agriculture, these products have yet to take over the market. Why? According to the ADC, one hurdle is having enough data available. – Photo: Mark Pasveer

Data accuracy a hurdle

Another thing is the data needs to be somehow standardised and read into a system. And most important, data needs to be accurate: “As an industry this is something we struggle with. Think of a yield file from a combine. Often you’re lucky if the correct crop type was captured in the data and the yield sensor was calibrated this season,” says the ADC.

Also read: Data ownership questions – and why they’re important

“Most people in precision ag have seen fields of 250-bushel yields in “soybean” fields where the operator didn’t bother to label it as corn. For the AI system to be able to recommend a variety we not only need the correct crop type documented but we also need to know what variety was actually planted, when, and at what population to really be able to train the algorithms and mine the data.”

_{Text continues underneath image}

If you have a big enough database of information, you can train a computer system to determine what the “best” variety is for a field. – Photo: Roel Dijkstra

Amount of variables

A third obstacle according to the ADC is the amount of variables to account for in farming.“Most obvious is weather. It isn’t too hard to have a record of what happened in a field from a weather standpoint. However, knowing what the coming season is going to look like is a little trickier.”

That means that you not only need to have all the different data about when, how, where, and which seed was planted, you also need to know if it is going to be a hot dry year, or cool and wet, says the ADC.

Forecast accuracy

Luckily, forecasts are continually improving. “Largely from the application of the same type of AI systems to model weather to know what is coming. However forecast accuracy is really only something you can really trust out a few days, or maybe a week or 2. Knowing what the next 6 months are going to be like is still fairly hard to predict for the most part, but again things are always getting better as the machines learn more.”

Also read: EU Code of Conduct on agricultural data sharing