Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Predicting Fruit Yield Using Shallow Neural Networks

Version 1 : Received: 1 September 2020 / Approved: 2 September 2020 / Online: 2 September 2020 (03:21:02 CEST)

How to cite: Bulanon, D.M.; Braddock, T.; Allen, B.; Bulanon, J.I. Predicting Fruit Yield Using Shallow Neural Networks. Preprints 2020, 2020090022 (doi: 10.20944/preprints202009.0022.v1). Bulanon, D.M.; Braddock, T.; Allen, B.; Bulanon, J.I. Predicting Fruit Yield Using Shallow Neural Networks. Preprints 2020, 2020090022 (doi: 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.

Subject Areas

Artificial neural network; image processing; machine vision; yield monitoring

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.