Banana Ripeness Classification Based on Color and Texture Using a CNN with ResNet50 Architecture
DOI:
https://doi.org/10.30871/jaic.v10i4.13508Keywords:
Banana Ripeness, Image Classification, ResNet50, Color and Texture Features, Confusion Matrix.Abstract
Banana ripeness is one of the main factors affecting fruit quality before distribution and consumption. Conventional ripeness assessment is generally performed through visual observation, which may lead to subjective and inconsistent results. Previous studies have reported promising results in banana ripeness classification. However, distinguishing adjacent ripeness stages remains challenging because of their similar visual characteristics. This study proposes a banana ripeness classification model using a Convolutional Neural Network (CNN) with the ResNet50 architecture. The dataset consisted of four ripeness categories, namely unripe, ripe, overripe, and rotten. Data balancing, preprocessing, and augmentation were applied before model training. A total of 1,120 images were used to train and evaluate the model. Transfer learning with full fine-tuning was employed to adapt pretrained visual features to different banana ripeness levels. The experimental results showed that the proposed model achieved an accuracy of 92.86%, while precision, recall, and F1-score reached 93%. Several misclassifications were observed between adjacent ripeness categories due to similarities in visual characteristics. These results indicate that the proposed ResNet50 model can effectively classify banana ripeness levels on the testing dataset based on color and texture information learned automatically from digital images.
Downloads
References
[1] R. Kosasih, Sudaryanto, and A. Fahrurozi, “Classification of six banana ripeness levels based on statistical features on machine learning approach,” International Journal of Advances in Applied Sciences, vol. 12, no. 4, pp. 317–326, Dec. 2023, doi: 10.11591/ijaas.v12.i4.pp317-326.
[2] W. Widyawati and R. Febriani, “Real-time detection of fruit ripeness using the YOLOv4 algorithm,” Teknika: Jurnal Sains dan Teknologi, vol. 17, no. 2, p. 205, Nov. 2021, doi: 10.36055/tjst.v17i2.12254.
[3] O. Martínez-Mora et al., “Artificial Vision-Based Dual CNN Classification of Banana Ripeness and Quality Attributes Using RGB Images,” Processes, vol. 13, no. 7, Jul. 2025, doi: 10.3390/pr13071982.
[4] M. Syifa Aryanta, C. A. Sari, and H. Rachmawanto, “A Banana Disease Detection Using MobileNetV2 Model Based on Adam Optimizer,” 2025. [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAIC
[5] R. E. Saragih and A. W. R. Emanuel, “Banana Ripeness Classification Based on Deep Learning using Convolutional Neural Network.”
[6] N. Nafi’iyah, R. Wardhani, and E. Prakasa, “Identification of Banana Ripeness using Convolutional Neural Network Approaches”, [Online]. Available: https://orcid.org/0000-0003-4685-6309
[7] Y. A. Nisa, C. A. Sari, E. H. Rachmawanto, and N. Mohd Yaacob, “Ambon Banana Maturity Classification Based On Convolutional Neural Network (CNN),” sinkron, vol. 8, no. 4, pp. 2568–2578, Oct. 2023, doi: 10.33395/sinkron.v8i4.12961.
[8] C. Dewi, S. Ueno, and K. Kato, “Artificially Ripeness Identification of Indonesian Banana Cultivar Using Convolution Neural Network,” in ACM International Conference Proceeding Series, Association for Computing Machinery, Oct. 2023, pp. 107–111. doi: 10.1145/3626641.3626668.
[9] C. A. Sari, L. Hamidah, S. Pradana, and E. H. Rachmawanto, “LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics Identifikasi Citra Jenis Rempah-Rempah Menggunakan Arsitektur RestNet50,” vol. 2, no. 1, pp. 27–41, 2025.
[10] E. Prasiwiningrum and Adyanata Lubis, “Classification Of Palm Oil Maturity Using CNN (Convolution Neural Network) Modelling RestNet 50,” Decode: Jurnal Pendidikan Teknologi Informasi, vol. 4, no. 3, pp. 983–999, Nov. 2024, doi: 10.51454/decode.v4i3.822.
[11] C. Pornpanomchai, “Image based durian (Durie azomethines Linn) sweetness measurement by ResNet50,” Engineering and Applied Science Research, vol. 51, no. 2, pp. 200–210, Jan. 2024, doi: 10.14456/easr.2024.20.
[12] S. Hardiyanti Putri, “ResNet50-Based Deep Learning Architecture with Focal Loss Optimization for Automated Fruit Ripeness Classification,” Jurnal SISFOKOM (Sistem Informasi dan Komputer), 2025, doi: 10.32736/sisfokom.v15i1.2449.
[13] M. Khalis Fikri et al., “Banana Ripeness Classification Using Convolutional Neural Network Based on Resnet-50,” Jurnal Media Elektrik, vol. 23, no. 2, pp. 157–165, Apr. 2026, doi: 10.59562/metrik.v23i2.11482.
[14] M. M. I. Al-Ghiffary, C. A. Sari, E. H. Rachmawanto, N. R. D. Cahyo, N. M. Yaacob, and R. R. Ali, “Milkfish Freshness Classification Using Convolutional Neural Networks Based on Resnet50 Architecture,” Advance Sustainable Science, Engineering and Technology, vol. 5, no. 3, Aug. 2023, doi: 10.26877/asset.v5i3.17017.
[15] P. Baglat, A. Hayat, F. Mendonça, A. Gupta, S. S. Mostafa, and F. Morgado-Dias, “Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review,” Jan. 01, 2023, MDPI. doi: 10.3390/s23020738.
[16] Tjokorda Istri Agung Pandu Yuni Maharani, I Gusti Agung Indrawan, Gede Dana Pramitha, Christina Purnama Yanti, and I Made Marthana Yusa, “Classification of Cavendish Banana Ripeness With CNN Method,” Indonesian Journal of Data and Science, vol. 6, no. 2, pp. 203–210, Jul. 2025, doi: 10.56705/ijodas.v6i2.259.
[17] S. Anatya, V. C. Mawardi, and J. Hendryli, “Fruit maturity classification using convolutional neural networks method,” in IOP Conference Series: Materials Science and Engineering, IOP Publishing Ltd, Dec. 2020. doi: 10.1088/1757-899X/1007/1/012149.
[18] R. Erz Saragih, A. Rezki Purnajaya, I. Syafrinal, and Y. Pernando, “Mango and Banana Ripeness Detection based on Lightweight YOLOv8.”
[19] M. Winklmair, R. Sekulic, J. Kraus, P. Penava, and R. Buettner, “A deep learning based approach for classifying the maturity of cashew apples,” PLoS One, vol. 20, no. 6 June, Jun. 2025, doi: 10.1371/journal.pone.0326103.
[20] B. A. Malabag, C. S. Santiago, E. L. Cahapin, J. L. Reyes, and G. S. Legaspi, “Fuzzy Logic-Based Size and Ripeness Classification of Banana using Image Processing Technique,” International Journal of Emerging Technology and Advanced Engineering, vol. 12, no. 10, pp. 11–18, Oct. 2022, doi: 10.46338/ijetae1022_02.
[21] B. Xiao, M. Nguyen, and W. Q. Yan, “Fruit ripeness identification using YOLOv8 model,” Multimed. Tools Appl., vol. 83, no. 9, pp. 28039–28056, Mar. 2024, doi: 10.1007/s11042-023-16570-9.
[22] C. A. Garillos-Manliguez and J. Y. Chiang, “Multimodal deep learning and visible-light and hyperspectral imaging for fruit maturity estimation,” Sensors (Switzerland), vol. 21, no. 4, pp. 1–18, Feb. 2021, doi: 10.3390/s21041288.
[23] G. Wang, Y. Gao, F. Xu, W. Sang, Y. Han, and Q. Liu, “A Banana Ripeness Detection Model Based on Improved YOLOv9c Multifactor Complex Scenarios,” Symmetry (Basel)., vol. 17, no. 2, Feb. 2025, doi: 10.3390/sym17020231.
[24] N. O. M. Salim and A. K. Mohammed, “Comparative Analysis of Classical Machine Learning and Deep Learning Methods for Fruit Image Recognition and Classification,” Traitement du Signal, vol. 41, no. 3, pp. 1331–1343, Jun. 2024, doi: 10.18280/ts.410322.
[25] L. Jambola, A. R. Darlis, W. Malaha, and D. Aryanta, “Classification system of banana types and ripeness levels based on convolutional neural network,” IAES International Journal of Artificial Intelligence, vol. 14, no. 6, pp. 4891–4901, Dec. 2025, doi: 10.11591/ijai.v14.i6.pp4891-4901.
[26] L. E. Chuquimarca, B. X. Vintimilla, and S. A. Velastin, “Assessing deep learning model robustness for banana ripeness classification under varying illumination conditions,” Smart Agricultural Technology, vol. 12, Dec. 2025, doi: 10.1016/j.atech.2025.101333.
[27] A. S and J. Kathirvelan, “Computer vision-based detection and classification of chemically ripened bananas and papayas at vendor site through deep learning AI models using real-time dataset,” Results in Engineering, vol. 26, Jun. 2025, doi: 10.1016/j.rineng.2025.104730.
[28] E. Tapia-Mendez, I. A. Cruz-Albarran, S. Tovar-Arriaga, and L. A. Morales-Hernandez, “Deep Learning-Based Method for Classification and Ripeness Assessment of Fruits and Vegetables,” Applied Sciences (Switzerland), vol. 13, no. 22, Nov. 2023, doi: 10.3390/app132212504.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Raafiandy Wirawan Avicenna, Christy Atika Sari, Eko Hari Rachmawanto
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
