AI for data analysis in agriculture
In the not-so-far-off future, artificial intelligence could help farmers analyze data to make decisions and improve their outputs.
“The bottleneck right now is that farmers have data but don’t necessarily know what it means. They often need a specialist to figure it out,” says Felippe Karp, a PhD candidate in McGill's Bioresource Engineering department and member of the Precision Agriculture and Sensor Systems (PASS) research team led by Professor Viacheslav Adamchuk.
Through a McGill, Telus and Olds College joint project, Karp is studying how to bring together multiple layers of farm data to support agricultural decision-making. “Having data from all commercially available sensors might not be practical for an individual farm,” . “One of the goals of this research is to identify which layers of data are most important to farm decision-making.”
Once researchers like Karp figure out what sensors and data are most useful, the AI platform would take over. Using farm data from these sensors as well as soil analysis, topography, combine yield maps, historical records on , products applied, weather, and costs for labour and machinery operation, the AI platform will help farmers manage for higher profits per acre, lower emissions, less labour per bushel — whatever goals the farm may have.
“We can’t predict exactly what will happen, but we can use past data to guide decisions based on probabilities,” Karp says. “Will it be right all of the time? No. But if farmers had a choice between 60 per cent chance of being right and a 20 per cent chance, they will go with the 60 per cent chance.”
With AI to help farmers synthesize high quality data from the most appropriate sources, Karp says, “farmers won’t have to guess any more.”
Though accurate and trustworthy AI guidance in farm decisions is still a ways off, experts like Karp envision that one day ‘precision agriculture’ will be synonymous with agriculture.
As Karp puts it, “Data would be part of the job of farming.”