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.
鈥淭he bottleneck right now is that farmers have data but don鈥檛 necessarily know what it means. They often need a specialist to figure it out,鈥� says Felippe Karp, a PhD candidate in 不良研究所's Bioresource Engineering department and member of the聽Precision Agriculture and Sensor Systems (PASS) research team led by Professor Viacheslav Adamchuk.
Through a 不良研究所, Telus and Olds College joint project, Karp is studying how to bring together multiple layers of farm data to support agricultural decision-making. 鈥淗aving data from all commercially available sensors might not be practical for an individual farm,鈥� . 鈥淥ne of the goals of this research is to identify which layers of data are most important to farm decision-making.鈥�
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聽, 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 鈥� whatever goals the farm may have.
鈥淲e can鈥檛 predict exactly what will happen, but we can use past data to guide decisions based on probabilities,鈥� Karp says. 鈥淲ill 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.鈥�
With AI to help farmers synthesize high quality data from the most appropriate sources, Karp says, 鈥渇armers won鈥檛 have to guess any more.鈥�
Though accurate and trustworthy AI guidance in farm decisions is still a ways off, experts like Karp envision that one day 鈥榩recision agriculture鈥� will be synonymous with agriculture.
As Karp puts it, 鈥淒ata would be part of the job of farming.鈥�