Advancing Fruit Image Classification with State-of-the-Art Deep Learning Techniques

Authors

  • Yunan Fauzi Wijaya Prodi Informatika, Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional, Jakarta, Indonesia
  • Djarot Hindarto Prodi Informatika, Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional, Jakarta, Indonesia https://orcid.org/0000-0001-7501-2610

DOI:

10.33395/sinkron.v8i2.13604

Keywords:

Convolutional Neural Network, Deep Learning, Image Classifier, Agricultural Technology, Computer Vision

Abstract

Fruit image classification technology using deep learning is making significant contributions in the agriculture and food retail sectors, promising to increase efficiency and productivity. However, there is an identified knowledge gap in dealing with the considerable variation in fruit appearance caused by factors such as type, size, color, and lighting conditions, as well as the precise identification of damage or disease. This research focuses on applying the developed Convolutional Neural Network architecture to fill this gap by using it in an extensive and diverse dataset, covering 67,692 image files categorized into 131 fruit classes. The training process showed substantial accuracy improvement, with training accuracy reaching 98.39% and validation accuracy at 90%, while training loss decreased to 0.0430 and validation loss to 0.2991. In the advanced stage of training, the training accuracy peaked at 99.43% in the 59th epoch with a shallow loss of 0.0251. However, the validation loss showed variation, indicating room for improvement in model generalization. The findings provide insight into the potential and challenges of applying Convolutional Neural Network models and fruit image classification with improved fruit sorting accuracy. Contribution to the literature in the field of information technology and agriculture by showing deep learning models can be improved to address the issue of fruit image variability.

GS Cited Analysis

Downloads

Download data is not yet available.

References

Alex Krizhevsky, Ilya Sutskever, G. E. H. (n.d.). ImageNet Classification with Deep Convolutional Neural Networks. NIPS’12: Proceedings of the 25th International Conference on Neural Information Processing Systems, 1, 1097–1105.

Clark, M. L., Salas, L., Baligar, S., Quinn, C. A., Snyder, R. L., Leland, D., Schackwitz, W., Goetz, S. J., & Newsam, S. (2023). Ecological Informatics The effect of soundscape composition on bird vocalization classification in a citizen science biodiversity monitoring project. 75(May 2022).

Coulibaly, S., Kamsu-foguem, B., Kamissoko, D., & Traore, D. (2022). Explainable deep convolutional neural networks for insect pest recognition. Journal of Cleaner Production, 371(July), 133638. https://doi.org/10.1016/j.jclepro.2022.133638

Hindarto, D. (2023a). Battle Models : Inception ResNet vs . Extreme Inception for Marine Fish Object Detection. 8(4), 2819–2826.

Hindarto, D. (2023b). Comparison of RNN Architectures and Non- RNN Architectures in Sentiment Analysis. Sinkron, 7(4), 2537–2546.

Hindarto, D. (2023c). Enhancing Road Safety with Convolutional Neural Network Traffic Sign Classification. Sinkron, 8(4), 2810–2818. https://doi.org/10.33395/sinkron.v8i4.13124

Hindarto, D. (2023d). Use ResNet50V2 Deep Learning Model to Classify Five Animal Species. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 7(4).

Hindarto, D., & Santoso, H. (2023). PyTorch Deep Learning for Food Image Classification with Food Dataset. 8(4), 2651–2661.

Kim, H., Jung, W. K., Park, Y. C., Lee, J. W., & Ahn, S. H. (2022). Broken stitch detection method for sewing operation using CNN feature map and image-processing techniques. Expert Systems with Applications, 188, 116014. https://doi.org/10.1016/j.eswa.2021.116014

Maqsood, M. H., Mumtaz, R., Haq, I. U., Shafi, U., Zaidi, S. M. H., & Hafeez, M. (2021). Super resolution generative adversarial network (Srgans) for wheat stripe rust classification. Sensors, 21(23), 1–12. https://doi.org/10.3390/s21237903

Muhammad Hammad Saleem, Johan Potgieter, K. M. A. (2016). Plant Disease Detection and Classification by Deep Learning. Nature, 29(7553), 1–73. http://deeplearning.net/

Nugroho, B., & Puspaningrum, E. Y. (2021). Kinerja Metode CNN untuk Klasifikasi Pneumonia dengan Variasi Ukuran Citra Input. Jurnal Teknologi Informasi Dan Ilmu Komputer, 8(3), 533. https://doi.org/10.25126/jtiik.2021834515

Soria, X., Sappa, A., Humanante, P., & Akbarinia, A. (2023). Dense extreme inception network for edge detection. Pattern Recognition, 139. https://doi.org/10.1016/j.patcog.2023.109461

Sze, E., Santoso, H., & Hindarto, D. (2022). Review Star Hotels Using Convolutional Neural Network. 7(1), 2469–2477.

Titi, R., Sari, K., Hindarto, D., & Nasional, U. (2023). Implementation of Cyber-Security Enterprise Architecture Food Industry in Society 5 . 0 Era. 8(2), 1074–1084.

Downloads


Crossmark Updates

How to Cite

Wijaya, Y. F. ., & Hindarto, D. (2024). Advancing Fruit Image Classification with State-of-the-Art Deep Learning Techniques. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(2), 1125-1134. https://doi.org/10.33395/sinkron.v8i2.13604

Most read articles by the same author(s)

1 2 3 4 > >>