BANANA SPECIES CLASSIFICATION USING NEURAL NETWORKS
Keywords:
Banana cultivar classification, Deep learning, Inception-V3Abstract
Indonesian bananas represent a strategic horticultural commodity with significant economic potential, yet post-harvest supply chains face persistent challenges due to subjective and labor-intensive manual sorting. To overcome these limitations, this study proposes an automated image-based classification framework evaluating eight Indonesian banana cultivars (Musa spp.). The computational pipeline integrates deep feature extraction via a pre-trained Inception-V3 model with a Multi-Layer Perceptron Artificial Neural Network (ANN) comprising three hidden layers. Evaluated across 1,020 test images using a 10-fold stratified sampling protocol, the model achieved a classification accuracy of 86.7%, a balanced $F_1$-score of 86.7%, and an Area Under the ROC Curve (AUC) of 0.985. Pisang Cavendish achieved the highest class-wise accuracy at 96.9%, whereas misclassifications occurred predominantly between morphologically similar cultivars, such as Pisang Kepok and Pisang Barangan. Overall, the low cross-entropy log loss (0.419) and high Matthews Correlation Coefficient (0.848) confirm strong prediction stability. These findings demonstrate that combining deep visual embeddings with neural networks offers a reliable, high-throughput tool for automated produce grading, supporting quality standardization and digitalization in agribusiness supply chains.
Keywords : Banana cultivar classification, Deep learning, Inception-V3
References
Alviansyz. (2020). tensorflow_jenis_pisang. https://www.kaggle.com/datasets/alviansyz/tensorflow-jenis-pisang
Daniel, I., Zendrato, G. E., & Manao, Y. (2025). CNN-Based Pet Image Classification: A Deep Learning Approach with Orange. Computer Science, Data Science And Arificial Intelligence, 2(2), 3031–9145.
Dewi, C., Arisoesilaningsih, E., Mahmudy, W. F., & Solimun. (2022). Identifying of unripe Ambon and Hijau banana fruits using computer vision and extreme learning machine classifier. IOP Conference Series: Earth and Environmental Science, 951(1). https://doi.org/10.1088/1755-1315/951/1/012031
Ergün, E. (2025). High precision banana variety identification using vision transformer based feature extraction and support vector machine. Scientific Reports, 15(1), 1–16. https://doi.org/10.1038/s41598-025-95466-0
Evans, E. A., Ballen, F. H., & Siddiq, M. (2020). Banana Production, Global Trade, Consumption Trends, Postharvest Handling, and Processing. In Handbook of Banana Production, Postharvest Science, Processing Technology, and Nutrition (pp. 1–18). Wiley. https://doi.org/10.1002/9781119528265.ch1
Fettig, J. S., Prayogo, C., Sirappa, M. P., Sukiman, Burhanuddin, Sultan, & Kurniawan, S. (2023). Ethnobotany of local banana (Musa spp.) variety Loka Pere in West Sulawesi, Indonesia. Biodiversitas, 24(12), 6472–6483. https://doi.org/10.13057/biodiv/d241209
Guo, J., Duan, J., Li, J., & Yang, Z. (2020). Mechanized technology research and equipment application of banana post-harvesting: A review. Agronomy, 10(3), 1–15. https://doi.org/10.3390/agronomy10030374
Hopid, H., Ekawati, I., Isdiantoni, I., Habibi, H., Zahoor, M., Patmawati, P., & Nyono, N. (2023). Farmers’ Local Knowledge in Accelerating Banana Fruiting to Increase Production Quality and Economic Benefit: A Case in Pragaan, Sumenep Regency, Indonesia. Sarhad Journal of Agriculture, 39(SpecialIssue 1), 113–122. https://doi.org/10.17582/JOURNAL.SJA/2023/39/S1.113.122
Huixian, J. (2020). The Analysis of Plants Image Recognition Based on Deep Learning and Artificial Neural Network. IEEE Access, 8, 68828–68841. https://doi.org/10.1109/ACCESS.2020.2986946
Ichsan, A., Riyadi, S., & Pardede, D. (2024). Analysis of Logistic Regression Regularization in Wild Elephant Classification with VGG-16 Feature Extraction. Journal of Computer Networks, Architecture and High Performance Computing, 6(2), 783–793. https://doi.org/10.47709/cnahpc.v6i2.3789
Kreuzberger, D., Kuhl, N., & Hirschl, S. (2023). Machine Learning Operations (MLOps): Overview, Definition, and Architecture. IEEE Access, 11(February), 31866–31879. https://doi.org/10.1109/ACCESS.2023.3262138
Martínez-Mora, O., Capuñay-Uceda, O., Caucha-Morales, L., Sánchez-Ancajima, R., Ramírez-Morales, I., Córdova-Márquez, S., & Cuenca-Mayorga, F. (2025). Artificial Vision-Based Dual CNN Classification of Banana Ripeness and Quality Attributes Using RGB Images. Processes, 13(7), 1–11. https://doi.org/10.3390/pr13071982
Melesse, T. Y. (2026). Intelligent Postharvest Sorting of Bananas Using Thermal Imaging and Deep Neural Network Models. Food and Bioprocess Technology, 19(2), 1–13. https://doi.org/10.1007/s11947-025-04173-1
Pamungkas, A., & Fadlil, A. (2023). Optimizing Banana Type Identification: An Support Vector Machine Classification-Based Approach for Cavendish, Mas, and Tanduk Varieties. Buletin Ilmiah Sarjana Teknik Elektro, 5(4), 539–551. https://doi.org/10.12928/biste.v5i4.9145
Pardede, D., Wanayumini, & Rosnelly, R. (2023). A Combination Of Support Vector Machine And Inception-V3 In Face-Based Gender Classification. International Conference on Information Science and Technology Innovation (ICoSTEC), 2(1), 34–39. https://doi.org/10.35842/icostec.v2i1.30
Pratama, R. N., Valencia, F. V., & Melita. (2025). PENGARUH KONSUMSI JANTUNG PISANG KEPOK TERHADAP PENINGKATAN PRODUKSI ASI PADA IBU MENYUSUI (STUDI KASUS) The Effect Of Consumption Of Kepok Banana Blossoms By Breastfeeding Mothers To Increase Breast Milk Production (Case Study). Masker Medika, 13(1), 24–30.
Rai, I. N., Mayadewi, N. N. A., Wiraatmaja, I. W., & Astiari, N. K. A. (2023). Fruit Morphology and Nutritional Composition of Different Genome Groups of Six Bananas Cultivars from Bali Island. Caraka Tani: Journal of Sustainable Agriculture, 38(2), 421–432. https://doi.org/10.20961/carakatani.v38i2.74941
Sulistyowarni, I., Sundari, S., & Halim, S. (2020). the Potential of Banana Trading Commodity To Fulfill Market Demand and Support Food Security in Defense Economic Perspective (Study in Bogor District). Jurnal Pertahanan & Bela Negara, 10(3), 307. https://doi.org/10.33172/jpbh.v10i3.1114
SUNARSI, D., NUGROHO, W., & WAHYUDI, D. (2023). BUSINESS OPPORTUNITIES AND ARRANGEMENT OF LEGAL DOCUMENTS IN LICENSING TOWARDS ECONOMIC RECOVERY POST DISASTER. ICCD, 5(1), 538–542. https://doi.org/10.33068/iccd.v5i1.603
Wisnubroto, D. S., Hermana, F., Saptowati, H., Utomo, U., Syaeful, H., Sumaryanto, A., Satmoko, A., Purwadi, M. D., Santoso, B., Santosa, P., Sunaryo, G. R., Sunarto, S., Rozali, B., Cahyono, A., & Sulistyo, J. B. (2024). Gamma irradiation pilot project: Enhancing food safety and export quality in Indonesia. Journal of Infrastructure, Policy and Development, 8(9), 1–26. https://doi.org/10.24294/jipd.v8i9.7661
Zainuddin, Z., Meilin, A., Asmaida, & Mulyani. (2025). An Analysis of NTMs Measures on Indonesian Banana and Pineapple Exports in Selected Asean, East Asian, and Middle Eastern Countries. Jurnal Manajemen Dan Agribisnis, 22(3), 261. https://doi.org/10.17358/jma.22.3.261
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Sugeng Riyadi, Irwan Daniel, Dia Adillia, Maini Wannisa Lubis
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
