Abstract Title

Fruit Yield Estimation Using Deep Neural Network

Additional Funding Sources

This research is supported by the Specialty Crop Block Grant of the Idaho State Department of Agriculture.

Abstract

One of the tools for precision agriculture is yield monitoring. In this paper, a yield monitoring system using machine vision is developed to estimate fruit yield early in the season. Predicting yield early in the season helps farmers in the marketing of their product and the production logistics. The machine vision system uses a color camera to acquire images of the trees during the blossom period. A deep neural network was developed to recognize and count the blossoms on the tree. There was a high correlation between the blossom count and the number of fruits on the tree which shows the potential of this method.

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Fruit Yield Estimation Using Deep Neural Network

One of the tools for precision agriculture is yield monitoring. In this paper, a yield monitoring system using machine vision is developed to estimate fruit yield early in the season. Predicting yield early in the season helps farmers in the marketing of their product and the production logistics. The machine vision system uses a color camera to acquire images of the trees during the blossom period. A deep neural network was developed to recognize and count the blossoms on the tree. There was a high correlation between the blossom count and the number of fruits on the tree which shows the potential of this method.