Skip to main content
Article | Green Intelligent Systems and Applications | Volume 6 - Issue 2 - 2026 | 268−287 | https://doi.org/10.53623/gisa.v6i2.1447
© 2026 by the author(s), and is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License Creative Commons Attribution 4.0 International (CC BY 4.0) License

Classification of Diseases in Oyster Mushroom Baglogs Using the MobileNetV3-Small Architecture

Author(s): Muhammad Vihir Mujahid , Ery Hartati
Author(s) information:
Faculty of Computer Science and Engineering, Universitas Multi Data, Palembang, South Sumatra, Indonesia

Corresponding author

Green Intelligent Systems and Applications

Volume 6 - Issue 2 - 2026

 About the journal
Submit your article

Oyster mushroom baglogs are highly susceptible to contamination by pathogens such as Trichoderma, Mucor, Neurospora, and Penicillium, while disease identification still relies primarily on subjective manual observation by farmers. This study evaluated the effect of data augmentation on the classification performance of the MobileNetV3-Small architecture for detecting diseases in oyster mushroom baglogs. A primary dataset comprising 900 independently collected images was organized into five classes: Healthy, Trichoderma, Mucor, Neurospora, and Penicillium. The dataset was divided at the physical baglog level into 600 training images (66.67%), 150 validation images (16.67%), and 150 testing images (16.67%) to prevent data leakage between subsets. Two experimental scenarios were compared under identical conditions: a baseline model without augmentation and an augmented model in which data augmentation was applied exclusively to the training set. Model training employed transfer learning using ImageNet-pretrained weights, beginning with feature extraction and followed by progressive fine-tuning. Classification performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrices. The results demonstrated that the baseline model achieved a test accuracy of 91.33% and an F1-score of 0.9127, whereas the augmented model achieved a higher test accuracy of 94.67% and an F1-score of 0.9466. All classification errors occurred between the visually similar Penicillium and Trichoderma classes, with data augmentation reducing the number of misclassified images from 13 to 8. Furthermore, both models correctly classified all test baglogs using majority voting across multiple image views. These findings demonstrate that data augmentation improves the classification performance of MobileNetV3-Small, particularly in distinguishing visually similar disease classes. The study provides a foundation for developing lightweight, augmentation-enhanced disease classification systems for oyster mushroom cultivation.

Read Full-Text
Previous article
Next article

Windriyati, R.D.H.; Kurniawan, A.; Farisi, H. (2025). Edukasi pengelolaan hama dan penyakit terpadu pada pelaku usaha jamur tiram di Desa Penolih, Kaligondang, Purbalingga. Madaniya, 6, 1447‒1454. https://doi.org/10.53696/27214834.1326.

Faturachman, I.; Kusumawati, R. (2024). Usaha budidaya jamur tiram. Ikraith-Ekonomika, 7, 65‒70. https://doi.org/10.37817/ikraith-ekonomika.v7i3.4250.

Mona, N.; Widyastuti, D.A.; Nurwahyunani, A.; Hayat, M.S. (2022). Analisis permasalahan umur baglog dan hama penyerang pada budidaya jamur tiram (Pleurotus ostreatus) di Kabupaten Semarang. Biodidaktika: Jurnal Biologi dan Pembelajarannya, 17. https://doi.org/10.30870/biodidaktika.v17i2.16497.

Anggraeni; Salmi; Helmi, H.; Robika. (2023). Optimalisasi usaha budidaya jamur tiram di Desa Teru melalui kemandirian pengadaan media tumbuh dan penanaman bibit jamur. Jurnal Pengabdian Masyarakat Borneo, 7, 69‒74. https://doi.org/10.35334/jpmb.v7i1.2998.

Ade, N.; Suranti, D.; Suryana, E. (2024). Implementasi metode certainty factor dalam mendeteksi hama dan penyakit pada jamur tiram putih. Jurnal Media Infotama, 20, 638‒645. https://doi.org/10.37676/jmi.v20i2.6570.

Naveed, M.U.; Jabbar, S.; Iqbal, M.M.; Awais, A.; Alkhazi, I.S.; Alghamdi, M. (2026). A hybrid lung and colon histopathological image classification framework using MobileNetV3-Small deep features and differential evolution optimization. Diagnostics, 16, 1256. https://doi.org/10.3390/diagnostics16091256.

Christnatalis; Lase, C.D.S.; Sitompul, T.H.; Hondro, A.P. (2024). Klasifikasi penyakit pada baglog jamur tiram menggunakan metode convolutional neural network. Jurnal Pendidikan dan Teknologi Indonesia, 4, 359‒367. https://doi.org/10.52436/1.jpti.480.

Shi, G.; Wang, Y.; Yang, Z.; Guo, Y. (2024). Wildfire identification based on an improved MobileNetV3-Small model. Forests, 15, 1975. https://doi.org/10.3390/f15111975.

Athallah, D.F.; Budiman, T.; Sianipar, A.Z. (2025). Optimizer evaluation for maize leaf disease using transfer learning with MobileNetV3-Small. Journal of Information Systems and Informatics, 7, 1939‒1954. https://doi.org/10.51519/journalisi.v7i2.1144.

Rakhmat, G.A.; Rizkiawarman, F. (2023). Implementasi arsitektur MobileNetV3 (studi kasus klasifikasi jamur beracun). Prosiding Diseminasi FTI Itenas, 3, 1‒14. Available online: https://eproceeding.itenas.ac.id/index.php/fti/article/view/3299.

Zhao, L.; Wang, L. (2022). A new lightweight network based on MobileNetV3. KSII Transactions on Internet and Information Systems, 16, 1‒15. https://doi.org/10.3837/tiis.2022.01.001.

Chin, P.-W.; Ng, K.-W.; Palanichamy, N. (2024). Plant disease detection and classification using deep learning methods: A comparison study. Journal of Informatics and Web Engineering, 3, 155‒168. https://doi.org/10.33093/jiwe.2024.3.1.10.

Pratama, R.; Harvianti, Y.; Rahayu, R.I.; Safitri, A.W. (2025). Cultivation of white oyster mushroom (Pleurotus ostreatus) in Indralaya. Sainmatika: Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam, 22, 164‒175. https://doi.org/10.31851/sainmatika.v22i2.20190.

Imanulloh, S.B.; Muslikh, A.R.; Setiadi, D.R.I.M. (2023). Plant diseases classification based leaves image using convolutional neural network. Journal of Computing Theories and Applications, 1, 1‒10. https://doi.org/10.33633/jcta.v1i1.8877.

Roseno, M.T.; Oktarina, S.; Nearti, Y.; Syaputra, H.; Jayanti, N. (2024). Comparing CNN models for rice disease detection: ResNet50, VGG16, and MobileNetV3-Small. Journal of Information Systems and Informatics, 6, 2099‒2109. https://doi.org/10.51519/journalisi.v6i3.865.

Howard, A.; Sandler, M.; Chu, G.; Chen, L.-C.; Chen, B.; Tan, M.; Wang, W.; Zhu, Y.; Pang, R.; Vasudevan, V.; Le, Q.V.; Adam, H. (2019). Searching for MobileNetV3. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea, 27 October–2 November 2019; pp. 1314‒1324. https://doi.org/10.1109/ICCV.2019.00140.

Salka, T.D.; Hanafi, M.; Rahman, S.M.S.A.A.; Zulperi, D.M.; Omar, Z. (2025). Plant leaf disease detection and classification using convolution neural networks model: A review. Artificial Intelligence Review, 58, 322. https://doi.org/10.1007/s10462-025-11234-6.

Shorten, C.; Khoshgoftaar, T.M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6, 60. https://doi.org/10.1186/s40537-019-0197-0.

Read Full-Text
About this article

SUBMITTED: 30 September 2026
ACCEPTED: 16 October 2026
PUBLISHED: 19 October 2026
SUBMITTED to ACCEPTED: 16 days
DOI: https://doi.org/10.53623/gisa.v6i2.1447

Cite this article
Mujahid, M. V., & Hartati, E. (2026). Classification of Diseases in Oyster Mushroom Baglogs Using the MobileNetV3-Small Architecture. Green Intelligent Systems and Applications, 6(2), 268−287. https://doi.org/10.53623/gisa.v6i2.1447
Citations
0
Share this article