Improving the Prediction of Rotten Fruit Using Convolutional Neural Network

Improving the Prediction of Rotten Fruit Using Convolutional Neural Network

© 2021 by IJETT Journal
Volume-69 Issue-7
Year of Publication : 2021
Authors : Sumitra Nuanmeesri, Lap Poomhiran, Kunalai Ploydanai
DOI :  10.14445/22315381/IJETT-V69I7P207

How to Cite?

Sumitra Nuanmeesri, Lap Poomhiran, Kunalai Ploydanai, "Improving the Prediction of Rotten Fruit Using Convolutional Neural Network" International Journal of Engineering Trends and Technology, vol. 69, no. 7, pp. 51-55, 2021. Crossref,

Separating rotten fruit is a necessary process that helps build trust and credibility before sending fresh products to consumers. This work proposed improving the model for predicting the rotten fruit that applied deep learning technique. The developed models were built using the VGG16 Convolutional Neural Network architecture with 3,000 images of fresh and rotten fruits for training, validating, and testing. The result showed that the developed model from the concatenated images dataset, which applied the RGB, Laplacian o Gaussian, GrabCut algorithm, and Hue Saturation Value with Adaptive Gaussian Thresholding, gave the higher model efficiency than the model that made from the RGB images dataset. This model has a validation accuracy of 89.97% and the testing accuracy of 91.33% for predicting the fresh or rotten red fruit.

Concatenated images, Convolutional Neural Network, Rotten fruit

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