Authors : R. Pradeep Kumar Reddy; S. Kiran; K. Neeraja; B. Chandra Sekhar Reddy; M. V. Varun Teja; M. Durga Prasad; S. Sai Harish

Volume/Issue : Volume 11 - 2026, Issue 2 - February

Google Scholar : https://tinyurl.com/48m86wcx

Scribd : https://tinyurl.com/m6d6ek98

DOI : https://doi.org/10.38124/ijisrt/26feb109

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Bananas serve as a crucial worldwide agricultural product, yet leaf diseases considerably hinder plant development and production. Timely and precise identification of diseases is crucial to halt their spread and promote sustainable faring practices. This review article discusses existing research on the identification of banana leaf diseases using image processing, machine learning, and deep learning techniques, using publicly available datasets and real-world images. Existing research suggests that deep learning-based methods generally outperform traditional methods. However, accuracy and generalization are highly impacted by class imbalance, preprocessing methods, and limitations of datasets. Automated disease identification systems provide effective solutions for identifying diseases in banana leaves. However, challenges such as limited datasets and real-world differences continue to exist, emphasizing the need for further research to ensure reliable agricultural applications.

Keywords : Banana Leaf Diseases; Machine Learning; Image Processing; Deep Learning.

References :

1. N. Jimenez, S. Orellana, B. Mazon-Olivo, W. Rivas-Asanza and I. Ramirez-Morales, “Detection of Leaf Diseases in Banana Crops Using Deep Learning Techniques,” AI, vol. 6, march 2025. doi: https://doi.org/10.3390/ai6030061
2. S. Meinam, N. Kishorjit and S. N. Basanta, “Optimized machine learning framework for detection of banana leaf disease,” Journal of Information Systems Engineering and Management, vol. 10, 2025.doi: https://doi.org/10.52783/jisem.v10i46s.8789
3. R. Alanazi, “A YOLOv10-based approach for banana leaf disease detection,” Engineering Technology and Applied Science Reserch, vol. 15, pp. 23522-23526, 2025.doi: https://doi.org/10.48084/etasar.11138
4. C. Unal, “Incorporating deep learning into the diagnosis of banana leaf spot diseases for the protection of banana crops,” Journal of Agriculltural sciences, vol. 31, no. 3, pp. 780-794, 2025.doi: https://doi.org/10.15832/ankutbd.1579442
5. R. Bhandari and a. Jadhaw, “Detection of Banana Leaf Disease and its Analysis Using Different Techniques,” RRJAAS, vol. 13, no. 1, April 2024.
6. N. Mduma and c. Elinisa, “Mobile-based convolutional neural network model for the early identification of banana diseases,” ScienceDirect, vol. 7, March 2024.doi: 10.1016/j.atech.2024.100423
7. J. Deng, w. Huang, G. Zhou, Y. Hu, L. Li and Y. Wang, “identification of banana leaf disease based on KVA and GR-ArNet,” Journal of Integrative Agriculture, vol. 23, no. 10, pp. 3554-3575, 2024.doi: 10.1016/j.jia.2023.11.037
8. V. Prabhakar and N. Sudha, “Identification of banana leaf diseases using image filtering algorithms,” Journal of Electrical Systems, vol. 20, pp. 836-842, 2024.
9. N. R. Rajalakshmi, S. Sravanan, J. Arunpandian, S. Kumar M, P. Jayagopal, s. Mallik and G. Qin, “Early Detection of Banana Leaf Disease Using Novel Deep Convolutional Neural Network,” Journal of Datascience and Intelligent Systems, August 2024.doi: 10.47852/bonviewJDSIS42021530
10. J. Thiagarajan, S. V. Kulkarni, S. Jadhav, A. A. Waghe, S. Raja, S. Subramaniam, H. Poddar and s. Rajagopal, “Analysis of banana plant health using machnine learning techniques,” Scientific reports, 2024.doi: https://doi.org/10.1038/s41598-024-63930-y
11. B. David and G. R, “Improved segmentation with optimization-based multilevel thresholding and K-means clustering for plant disease identification,” SSRN, 2023.doi:https://doi.org/10.21203/rs.3.rs-2373358/v1
12. M. A. Bhuiyan, H. M. Abdullah, S. Arman, S. Rahaman and K. A. Mahmud, “BananaSqueezeNet: A very fast lightweight convolutional neural network for the diagnisis of three prominent banana leaf diseases,” Smart agriculture Technology, vol. 4, pp. 2772-3755, 2023.doi:https://doi.org/10.1016/j.atech.2023.100214
13. H. R. Chetan, G. S. Rajanna, B. R. Sreenivasa and N. Y. Ganesh, “Plant disease detection using deep learning in banana and sunflower,” Journal of Advanced Zoology, vol. 44, no. 03, pp. 93-106, 2023.doi: https://doi.org/10.17762/jaz.v44i3.369
14. V. Chaudari and M. P. Patil, “Detection and classification of banana leaf disease using novel segmentation and ensemble machine learning approach,” Applied Computer Systems, vol. 28, pp. 92-99, 2023.doi: https://doi.org/10.2478/acss-2023-0009
15. V. Gokula Krishnan, R. Venkateswara, D. V., S. Kaviarasan and D. J., “An automated segmentation and classification model for banana leaf disease detection,” Journal of applied Biology and Biotechnology, vol. 10(1), pp. 213-220, 2022.doi:https://doi.org/10.7324/JABB.2021.100126
16. K. Sahu and S. Minz, “Adaptive fusion of K-means region growing with optimized deep features for enhanced LSTM-based multi disease classification of plant leaves,” Geocarto International, vol. 38, 2023.doi: 10.1080/10106049.2023.2178520
17. R. Sangeetha, J. Logeshwaran, J. Rochar and J. Lloret, “An Improved Agro Deep Learning Model for Detection of Panama Wilts Disease in Banana Leaves,” Journal of Agricultural Engineering, vol. 5, pp. 660-679, 2023.doi: https://doi.org/10.3390/agriengineering5020042
18. U. F. Jorge, M.-Z. Maria, C.-C. Juan and O. D. Daniel, “Effective methods based on distinct learning principles for the analysis of hyperspectral images to detect Black Sigatoka disease,” Plants, vol. 11, no. 19, 2022. https://doi.org/10.3390/plants11192581
19. L. Narayanan, S. R, Y. H. Robinson, S. Vimal, S. V., M. Kaliappan and G. Julie, “Banana plant disease classification using hybrid convolutional neural networks,” Computational Intelligence and Neuroscience, no. 1, 2022doi: 10.1155/2022/9153699.
20. V. B. C. Calou, A. D. S. Teixiera, L. C. J. Moreira, C. Lima, J. Oliveira and M. Oliveira, “The use of UAVs in monitoring Yellow Sigatoka in banana,” Biosystems Engineering, vol. 193, pp. 115-125, 2020.doi:https://doi.org/10.1016/j.biosystemseng.2020.02.016
21. S. P. Deenan, J. SatheeshKumar and S. Nagachandrabose, “Image segmentation algorithms for banana leaf diseases diagnosis,” Journal of the Institution of Engineers(India):Series C, 2020.doi: 10.1007/s40032-020-005292-5
22. D. C. S. Kumar, D. Mathew and A. Cherian, “Foliar fungal disease classification in banana plants using elliptical local binary pattern on multiresolution dual-tree complex wavelet transform domain,” Information Processing in Agriculture, 2020. https://doi.org/10.1016/j.inpa.2020.11.002
23. T. Yonow, Ramirez-Villegas J, Abadie C, Darnell RE, Ota N and Kriticos DJ, “Black Sigatoka in bananas: Ecoclimatic suitability and disease pressure assignments,” PLoS ONE 14(18):e0220601, 2019.doi:https://doi.org/10.1371/journal.pone.0220601
24. J. Amara, B. Bouaziz and A. Algergawy, “deep learning-based approach for banana leaf disease classification,” Lecture Notes in Informatics(LNI), pp. 79-88, 2017.