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One of the main challenges in tree fruit orchards is to accurately predict apple yield and identify the health of individual trees (e.g., healthy foliage, fruit development, detecting and identifying diseased trees). Manual performance of these tasks is labour intensive and costly. Therefore, automated processes provide novel solutions with enhanced accuracy, efficiency, and productivity. The proposed solution is to develop an automated system that utilizes machine vision and artificial intelligence strategies to accurately count flower blooms and apples, detect and identify diseased fruit and trees, and improve yield estimates for producers. Automating this process will reduce labour costs and improve apple yields for producers. The system will provide truly meaningful and easy-to-digest information to the farmer about the orchard, including the predicted productivity, suggested trimming, and possible growing issues (such as unhealthy trees or areas that require more attention).
Andrew Gadsden;Mohammad Biglarbegian;John Cline
Dr. Robot Inc.
University of Guelph
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