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Deep Learning Artificial Neural Networks for Detection of Fruit Maturity Stage in Wild Blueberries

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

Citation:  2021 ASABE Annual International Virtual Meeting  2100815.(doi:10.13031/aim.202100815)
Authors:   Craig B MacEachern, Travis J Esau, Arnold W Schumann, Patrick J Hennessy, Qamar U Zaman
Keywords:   App, fruit, quality, ripeness, YOLO.

Abstract. Optimal harvest windows for wild blueberries (Vaccinium angustifolium Ait.) can be as small as a few days to ensure peak ripeness. Confirming this critical timing is one of the most important steps in harvesting wild blueberries. If the harvest window is mistimed it can result in a significant portion of the berries being under or overripe and ultimately unmarketable. In this study, four different convolutional neural networks (YOLOv3, YOLOv3-Tiny, YOLOv3-SPP and YOLOv4) were trained on the Darknet deep learning framework and compared. Each of the networks were trained to recognize green (unripe), red (unripe) and blue (ripe) berries from a series of 6,766 labelled images. These images were cropped from 337 high resolution images taken across four commercial wild blueberry field sites on eight days during July and August of 2018 and 2019. Independent testing yielded the best results with the YOLOv3-SPP network. YOLOv3-SPP achieved AP scores of 88.40% (blue), 78.91% (green), 66.37% (red) for a mAP score of 77.90%. Data from this study in combination with yield data will be integrated into a mobile phone app which will be available for determining harvest timings and estimating yields.

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