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All Journal MIND (Multimedia Artificial Intelligent Networking Database) Journal Paradigma
DWI ROLLIAWATI
Program Studi Sistem Informasi, Universitas Islam Negeri Sunan Ampel, Surabaya.
Computer Science & IT

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Classification of Fresh and Rotten Fruits and Vegetables Using a Customized Convolutional Neural Network M SYAFIQ UBAIDILLAH; REYHAN DANY INDRARTO; DWI ROLLIAWATI; AHMAD YUSUF
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 10, No 1 (2025): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v10i1.34-47

Abstract

This research aims to develop a customized Convolutional Neural Network (CNN) model based on ResNet-18 for classifying fruit and vegetable types and freshness. The Fresh and Rotten dataset was used to train and test the model, consisting of 30,357 images across nine fruit and vegetable categories. The model employs three additional blocks to enhance classification capabilities. The study results indicate an average accuracy of 98% for freshness classification and 99% for fruit and vegetable type classification, with consistent training and validation performance. Data augmentation and normalization methods also improved the model's generalization capabilities. These findings highlight CNN's potential as a reliable tool for agricultural product management, supporting efficient distribution and maintaining product quality.
Classification of Vegetable Types Using the Convolutional Neural Network (CNN) Algorithm Wilis Arum Karunia; Safriya Murni Puspita; Dwi Rolliawati; Ahmad Yusuf
Paradigma - Jurnal Komputer dan Informatika Vol. 27 No. 1 (2025): March 2025 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v27i1.7577

Abstract

This study aims to classify vegetable types using the Convolutional Neural Network (CNN) algorithm with a dataset encompassing 15 vegetable classes and a total of 31,000 images. By utilizing the TensorFlow and Keras libraries, the CNN model was designed with convolutional, pooling, and dense layers to recognize visual features such as color, texture, and shape. The results indicate a highest validation accuracy of 95.83% and a testing accuracy of 93%. This research contributes to the application of the CNN algorithm for image classification and demonstrates its potential in handling multi-class datasets effectively. However, since the vegetables used have very distinct shapes and textures, this study is more relevant in the context of the technical application of the CNN algorithm rather than practical benefits. The research would be more impactful if applied to vegetables with similar shapes and characteristics, thereby supporting farmers or individuals studying vegetable traits in greater depth. Additionally, such an approach could address challenges in differentiating visually similar vegetable types, making the technology more valuable in real-world agricultural or educational settings.
Co-Authors AHMAD YUSUF M SYAFIQ UBAIDILLAH REYHAN DANY INDRARTO Safriya Murni Puspita Wilis Arum Karunia