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All Journal International Journal of Advances in Applied Sciences International Journal of Advances in Data and Information Systems
Awang, Wan Suryani Wan
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Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Journal : International Journal of Advances in Applied Sciences

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Comparative study on fine-tuning deep learning models for fruit and vegetable classification Mamat, Abd Rasid; Mohamed, Mohamad Afendee; Kadir, Mohd Fadzil Abd; Rawi, Norkhairani Abdul; Aziz, Azim Zaliha Abd; Awang, Wan Suryani Wan
International Journal of Advances in Applied Sciences Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i2.pp384-393

Abstract

Fruit and vegetable recognition and classification can be a challenging task due to their diverse nature and have become a focal point in the agricultural sector. In addition to that, the classification of fruits and vegetables increases the cost of labor and time. In recent years, deep learning applications have surged to the forefront, offering promising solutions. Particularly, the classification of fruits using image features has garnered significant attention from researchers, reflecting the growing importance of this area in the agricultural domain. In this work, the focus was on fine-tuning hyperparameters and the evaluation of a state-of-the-art deep convolutional neural network (CNN) for the classification of fruits and vegetables. Among the hyperparameters studied are the number of batch size, number of epochs, type of optimizer, rectified unit, and dropout. The dataset used is the fruit_vegetable dataset which consists of 36 classes and each class contains 1,000 images. The results show that the proposed model based on the batch size=64 and the number of epochs=25, produces the most optimal model with an accuracy value (training) of 99.02%, while the validation is 95.73% and the loss is 6.06% (minimum).
Co-Authors Aziz, Azim Zaliha Abd Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Kadir, Mohd Fadzil Abd Kuswardana, Dendy Arizki Mamat, Abd Rasid Mohamed, Mohamad Afendee Rawi, Norkhairani Abdul Trimono, Trimono