AI Case Study
McCain will test Resson's image recognition algorithms for crop condition monitoring to optimise potato yields and reduce costs
McCain Foods has planned to test Resson's image recognition system to assess potato crop health using ground sensors and a variety of imagery, including that taken from satellites, drones and tractor cameras. Ressson claims its pest and disease detection and classification is more accurate than a human's.
From The Globe and Mail: "Using photos of crops – from tractor cameras, drones or satellite imagery – Resson has developed image-processing technology that, combined with ground-sensor data, uses large-scale cloud-based data processing to help farmers assess crop production and field conditions as well as understand what diseases could be coming to their farms. Eventually, the technology could be harvested to help farmers achieve maximum return on investment for their businesses, such as by helping farmers figure out how much water or herbicide they should use on a given field... [McCain had announced] it would test the technology on its farms in 2016, and will try to refine it to optimize potato yields and lower costs. Wider implementation across McCain-affiliated farms could happen after that."
McCain's CEO Dirk Van de Put states that the company is "trying to optimize agriculture as it relates to potatoes – meaning using as few inputs as possible, like [not overusing] water, pesticides, fertilizers and so on, and Resson's technology will help with that" as reported in The Globe and Mail.
Planned in 2016, results undisclosed
Using photos of crops – from tractor cameras, drones or satellite imagery – the company has developed image-processing technology that, combined with ground-sensor data, uses large-scale cloud-based data processing to help farmers assess crop production and field conditions.
According to The Globe and Mail, "multiple levels of imagery and ground sensors to assess crop health".