Bulanon, D.M.; Braddock, T.; Allen, B.; Bulanon, J.I. Predicting Fruit Yield Using Shallow Neural Networks. Preprints2020, 2020090022. https://doi.org/10.20944/preprints202009.0022.v1
APA Style
Bulanon, D.M., Braddock, T., Allen, B., & Bulanon, J.I. (2020). Predicting Fruit Yield Using Shallow Neural Networks. Preprints. https://doi.org/10.20944/preprints202009.0022.v1
Chicago/Turabian Style
Bulanon, D.M., Brice Allen and Joseph Ichiro Bulanon. 2020 "Predicting Fruit Yield Using Shallow Neural Networks" Preprints. https://doi.org/10.20944/preprints202009.0022.v1
Abstract
Precision agriculture is a technology used by farmers to help food sustainability amidst growing population. One of the tools of precision agriculture is yield monitoring, which helps a farmer manage his production. Yield monitoring is usually done during harvest, however it could also be done early in the growing season. Early prediction of yield, specifically for fruit trees, aids the farmer in the marketing of their product and assists in managing production logistics such as labor requirement and storage needs. In this study, a machine vision system is developed to estimate fruit yield early in the season. The machine vision system uses a color camera to capture images of fruit trees during the full bloom period. An image segmentation algorithm based on an artificial neural network was developed to recognize and count the blossoms on the tree. The artificial neural network segmentation algorithm uses color information and position as input. The resulting correlation between the blossom count and the actual number of fruits on the tree shows the potential of this method to be used for early prediction of fruit yield.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
