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.
āThe bottleneck right now is that farmers have data but donāt necessarily know what it means. They often need a specialist to figure it out,ā says Felippe Karp, a PhD candidate in ŗ«¹śĀćĪč's Bioresource Engineering department and member of theĀ Precision Agriculture and Sensor Systems (PASS) research team led by Professor Viacheslav Adamchuk.
Through a ŗ«¹śĀćĪč, Telus and Olds College joint project, Karp is studying how to bring together multiple layers of farm data to support agricultural decision-making. āHaving data from all commercially available sensors might not be practical for an individual farm,ā . āOne of the goals of this research is to identify which layers of data are most important to farm decision-making.ā
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 onĀ , 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 ā whatever goals the farm may have.
āWe canāt predict exactly what will happen, but we can use past data to guide decisions based on probabilities,ā Karp says. āWill 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.ā
With AI to help farmers synthesize high quality data from the most appropriate sources, Karp says, āfarmers wonāt have to guess any more.ā
Though accurate and trustworthy AI guidance in farm decisions is still a ways off, experts like Karp envision that one day āprecision agricultureā will be synonymous with agriculture.
As Karp puts it, āData would be part of the job of farming.ā