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Atta Ullah Khan
Hamdard University Islamabad, Pakistan
Computer Science & IT

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Journal : Journal of Soft Computing Exploration

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A new CNN model integrated in onion and garlic sorting robot to improve classification accuracy Apri Dwi Lestari; Atta Ullah Khan; Dwika Ananda Agustina Pertiwi; Much Aziz Muslim
Journal of Soft Computing Exploration Vol. 5 No. 1 (2024): March 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i1.304

Abstract

The profit share of the vegetable market, which is quite large in the agricultural industry, needs to be equipped with the ability to classify types of vegetables quickly and accurately. Some vegetables have a similar shape, such as onions and garlic, which can lead to misidentification of these types of vegetables. Through the use of computer vision and machine learning, vegetables, especially onions, can be classified based on the characteristics of shape, size, and color. In classifying shallot and garlic images, the CNN model was developed using 4 convolutional layers, with each layer having a kernel matrix of 2x2 and a total of 914,242 train parameters. The activation function on the convolutional layer uses ReLu and the activation function on the output layer is softmax. Model accuracy on training data is 0.9833 with a loss value of 0.762.
Co-Authors Apri Dwi Lestari Dwika Ananda Agustina Pertiwi Much Aziz Muslim