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http://dx.doi.org/10.12791/KSBEC.2022.31.4.270

Grading of Harvested 'Mihwang' Peach Maturity with Convolutional Neural Network  

Shin, Mi Hee (Institute of Agriculture and Life Sciences, Gyeongsang National University)
Jang, Kyeong Eun (Division of Applied Life Science, Graduate School of Gyeongsang National University)
Lee, Seul Ki (Fruit Research Division, National Institute of Horticultural and Herbal Science)
Cho, Jung Gun (Fruit Research Division, National Institute of Horticultural and Herbal Science)
Song, Sang Jun (Farm&Farm Soft)
Kim, Jin Gook (Department of Horticulture, College of Agriculture and Life Science, Gyeongsang National University)
Publication Information
Journal of Bio-Environment Control / v.31, no.4, 2022 , pp. 270-278 More about this Journal
Abstract
This study was conducted using deep learning technology to classify for 'Mihwang' peach maturity with RGB images and fruit quality attributes during fruit development and maturation periods. The 730 images of peach were used in the training data set and validation data set at a ratio of 8:2. The remains of 170 images were used to test the deep learning models. In this study, among the fruit quality attributes, firmness, Hue value, and a* value were adapted to the index with maturity classification, such as immature, mature, and over mature fruit. This study used the CNN (Convolutional Neural Networks) models for image classification; VGG16 and InceptionV3 of GoogLeNet. The performance results show 87.1% and 83.6% with Hue left value in VGG16 and InceptionV3, respectively. In contrast, the performance results show 72.2% and 76.9% with firmness in VGG16 and InceptionV3, respectively. The loss rate shows 54.3% and 62.1% with firmness in VGG16 and InceptionV3, respectively. It considers increasing for adapting a field utilization with firmness index in peach.
Keywords
convolutional neural networks; deep learning; GoogLeNet; maturity; robot harvest;
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