Date fruit type classification using convolutional neural networks

Date fruit type classification using convolutional neural networks

Abdullah ALAVI, Md Faysal AHAMED, Ali ALBELADI, Mohamed MOHANDES

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Abstract. Classification of objects is an important task for convolutional neural networks (CNNs). They have been applied to numerous fields with excellent results. In this study, we use CNNs to classify five categories of Sukkari dates, namely Galaxy, Mufattal, Nagad, Qishr, and Ruttab. Transfer learning is when a pretrained model is taken and only the final layers are trained to make a prediction. In this paper, we used the following five models: SqueezeNet, GoogLeNet, EfficientNet-b0, ShuffleNet, and MobileNet V2. The results show that SqueezeNet outperforms the other networks with a classification accuracy of 92% on the testing set. The testing accuracy for GoogLeNet, EfficientNet-b0, ShuffleNet, and MobileNet V2, on the other hand are 85.14%, 82.86%, 89.14%, and 87.43%, respectively. As this is a classification task, other metrics like precision, recall, and F1 score are also evaluated. These values for the SqueezeNet on the testing set are 92.67%, 92%, and 92.33%, respectively. ShuffleNet was second with values of 89.41%, 89.14%, and 89.28%, respectively. EfficientNet scored the lowest with 83.10%, 82.86%, and 82.98%, respectively.

Date Fruit Type Classification, Convolutional Neural Network, Squeezenet, Pretrained Network, Transfer Learning

Published online 7/15/2024, 9 pages
Copyright © 2024 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: Abdullah ALAVI, Md Faysal AHAMED, Ali ALBELADI, Mohamed MOHANDES, Date fruit type classification using convolutional neural networks, Materials Research Proceedings, Vol. 43, pp 205-213, 2024


The article was published as article 27 of the book Renewable Energy: Generation and Application

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

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