Image-Based Classification of Soil Crops of Vegetables Using a Learning Machine

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Alifan Alifan
Abd Ghofur
Ahmad Afif azmi
Ahmad fauzan

Abstract

Indonesia as an agricultural country has great potential in the agricultural sector, especially olericultural vegetables which include leaf, fruit, flower, and tuber vegetables. The lack of utilization of technology in the identification of vegetable types is an obstacle in the process of education and automatic visual classification. This research aims to develop an image classification model of olericultural vegetables using Google's Teachable Machine platform. The dataset was manually collected from Google and Pinterest, consisting of 240 images divided into four classes. After labeling, the data was trained using the standard parameters in Teachable Machine: 50 epochs, batch size 16, and learning rate 0.01. Test results on 12 test images show that the model can recognize most images quite accurately, although there are some missed predictions.The results show that Teachable Machine can be a simple alternative in web-based image classification, although there are still limitations in accuracy and data generalization. This model is suitable for use as an initial educational tool in the introduction of artificial intelligence technology in agriculture.

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How to Cite
Alifan, A., Abd Ghofur, Ahmad Afif azmi, & Ahmad fauzan. (2025). Image-Based Classification of Soil Crops of Vegetables Using a Learning Machine. LOREM: Computational Engineering and Computer Information Systems, 2(1), Pages 1 - 6. https://ejournal.mediakunkun.com/index.php/lorem/article/view/268
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How to Cite

Alifan, A., Abd Ghofur, Ahmad Afif azmi, & Ahmad fauzan. (2025). Image-Based Classification of Soil Crops of Vegetables Using a Learning Machine. LOREM: Computational Engineering and Computer Information Systems, 2(1), Pages 1 - 6. https://ejournal.mediakunkun.com/index.php/lorem/article/view/268

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