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Fruit Yield Prediction Using Artificial Intelligence

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  2019 ASABE Annual International Meeting  1900583.(doi:10.13031/aim.201900583)
Authors:   Trevor Braddock, Sam Roth, Joseph Ichiro Bulanon, Brice Allen, Duke M Bulanon
Keywords:   Artificial intelligence, image processing, machine vision, neural networks, yield estimation

Abstract.

One of the tools for precision agriculture is yield monitoring. Early prediction of yield aids the farmer in the marketing of their product and assists in managing production logistics. A yield monitoring system using machine vision is developed to estimate fruit yield early in the season. The machine vision system uses a color camera to acquire images of the trees during the blossom period. An image segmentation algorithm was developed to then recognize and count the blossoms on the tree. This shallow neural network segmentation algorithm uses color information and position as input. The resulting high correlation between the blossom count and the number of fruits on the tree shows the potential of this method.

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