Farmers see how AI technology can help with water management

WOOD RIVER, Neb. (KSNB) - The use of AI technology is rapidly growing across multiple fields, including the agricultural industry. Initially, it’s being introduced to farmers as a way to help with water management.

According to Lindsay’s Senior Project Manager, Justin Gibson, only one-third of farmers are using a quantitative method to determine when, where, and how much to irrigate. Most rely on qualitative approaches, meaning they’re only checking the soil or going by when they irrigated last year.

Gibson said AI technology will help farmers incorporate more tech into their practices, reducing potentially incorrect decisions made in the field.

The AI will calibrate the next steps and determine how much water to use. Gibson said over time, if farmers agree with AI’s prediction analysis, they’ll be more likely to adopt the technology in everyday use.

“What we can do on the prediction side is bring in a lot of different data screens that are relevant for irrigation scheduling,” said Gibson. “So, one of them is going to be weather, if it’s hot outside you know the crop can get a bit thirstier and use more water, you know the opposite of that if it’s really cool and rainy.”

Gibson said bringing in weather data is critical to proper water measurements, and Lindsay offers a product called ‘FieldNet Advisor,’ which utilizes AI to monitor irrigation and it’s accessible by cell phone.

“For our product FieldNet Advisor, what it does is it um, it’s a computer simulation and it um it simulates how much moisture is in the soil by doing a water balance,” said Gibson. " So, we look at the amount of water that’s coming in, what you’re irrigating, rainfall, and the amount that’s leaving.”

He said the process of soil moisture balance is similar to balancing a checkbook.

“For AI technology, you know one one application of that is to look at how an individual has managed their farm with irrigation in the past. We can take that information and make better recommendations that are more similar to how a grower might make the recommendation in the future.”

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