A Western study could help farmers get out of a potential jam by using artificial intelligence (AI) and passive camera monitoring to enhance strawberry cultivation.
In a paper published in Foods, Western engineers describe a new machine-learning approach that yields the highest-ever precision and accuracy rates for ripeness and disease detection in strawberries of any previous attempts.
AI and computer vision have been used to monitor a variety of different crops over the past two decades and most of the time the surveillance is done by highly expensive, third-party, private companies taking control out of the hands of farmers.
Joshua Pearce, the John M. Thompson Chair in Information Technology and Innovation at Western Engineering and Ivey Business School; Soodeh Nikan, a Western electrical and computer engineering professor; and their collaborators are working on a new project. And their approach is available to everyone. For free.
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