Click on “Download PDF” for the PDF version or on the title for the HTML version.

If you are not an ASABE member or if your employer has not arranged for access to the full-text, Click here for options.

Fruit Yield Prediction Using Artificial Intelligence

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

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


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.

(Download PDF)    (Export to EndNotes)