A Review on Deep Learning Framework for Automated Classification of Banana Leaf Diseases
DOI:
https://doi.org/10.32628/CSEIT26124Keywords:
Banana leaf disease, Deep learning, Image classification, Plant pathology, Smart agricultureAbstract
Banana is one of the most economically significant fruit crops worldwide, yet its productivity is severely affected by a wide range of leaf diseases that compromise yield and quality. Traditional disease identification methods rely on expert visual inspection, which is time-consuming, subjective, and often inaccessible to small-scale farmers. Recent advances in deep learning have enabled automated, accurate, and scalable solutions for banana leaf disease classification using digital images. This review presents a comprehensive analysis of deep learning frameworks developed for automated banana leaf disease detection and classification. The paper systematically examines convolutional neural networks, lightweight architectures, hybrid CNN–Transformer models, object detection frameworks, and explainable AI techniques applied to banana leaf images. Additionally, datasets, preprocessing strategies, performance evaluation metrics, and deployment considerations are discussed. The review highlights key research findings, identifies major challenges such as dataset imbalance, environmental variability, and model generalization, and outlines future research directions to enhance robustness and real-world applicability. This study aims to serve as a valuable reference for researchers and practitioners working on intelligent agricultural disease diagnosis systems.
Downloads
References
L. Techniques, “Detection of Leaf Diseases in Banana Crops Using Deep Learning Techniques,” AI, 2025, doi: 10.3390/ai6030061.
S. Murugesan, J. Chinnadurai, and S. Srinivasan, “Robust multiclass classification of crop leaf diseases using hybrid deep learning and Grad-CAM interpretability,” Scientific Reports, pp. 1–22, 2025, doi: 10.1038/s41598-025-14847-7. DOI: https://doi.org/10.1038/s41598-025-14847-7
L. Techniques, “Detection of Leaf Diseases in Banana Crops Using Deep Learning Techniques,” AI, 2025, doi: 10.3390/ai6030061. DOI: https://doi.org/10.3390/ai6030061
T. Pham, T. Nguyen, and V. Nguyen, “Ambiguity-aware semi-supervised learning for leaf disease classification,” Scientific Reports, pp. 1–17, 2025, doi: 10.1038/s41598-025-95849-3. DOI: https://doi.org/10.1038/s41598-025-95849-3
C. M. Zheng et al., “Banana Leaf Disease Classification Using SqueezeNet, AlexNet and MobileNet,” JOURNAL OF ELECTRONIC VOLTAGE AND APPLICATION, vol. 6, no. 1, pp. 48–58, 2025. DOI: https://doi.org/10.30880/jeva.2025.06.01.005
N. R. Rajalakshmi and S. Saravanan, “Early Detection of Banana Leaf Disease Using Novel Deep Convolutional Neural Network,” Journal of Data Science and Intelligent Systems, vol. 3, no. August 2023, pp. 192–199, 2025, doi: 10.47852/bonviewJDSIS42021530. DOI: https://doi.org/10.47852/bonviewJDSIS42021530
J. J. Mora, G. Blomme, N. Safari, S. Elayabalan, R. Selvarajan, and M. G. Selvaraj, “Digital framework for georeferenced multiplatform surveillance of banana wilt using human in the loop AI and YOLO foundation models,” Scientific Reports, pp. 1–30, 2025, doi: 10.1038/s41598-025-87588-2. DOI: https://doi.org/10.1038/s41598-025-87588-2
A. N. Experimental et al., “AN EXPERIMENTAL EVALUATION OF IMAGE FILTERING ALGORITHMS FOR,” ARJST, vol. 1, no. April, pp. 1–9, 2025, doi: 10.13140/RG.2.2.31569.85603.
Z. Zeng, T. Mahmood, Y. Wang, A. Rehman, and M. A. Mujahid, “AI-driven smart agriculture using hybrid transformer-CNN for real time disease detection in sustainable farming,” Scientific Reports, pp. 1–21, 2025, doi: 10.1038/s41598-025-10537-6. DOI: https://doi.org/10.1038/s41598-025-10537-6
R. Alanazi, “A YOLOv10-based Approach for Banana Leaf Disease Detection,” Engineering, Technology & Applied Science Research, vol. 15, no. 3, pp. 23522–23526, 2025. DOI: https://doi.org/10.48084/etasr.11138
M. S. Aryanta, C. A. Sari, and E. H. Rachmawanto, “A Banana Disease Detection Using MobileNetV2 Model Based on Adam Optimizer,” Journal of Applied Informatics and Computing (JAIC), vol. 9, no. 4, pp. 1207–1218, 2025. DOI: https://doi.org/10.30871/jaic.v9i4.9870
B. Charles, O. Mustapha, M. Lawan, K. Adisa, O. Sani, and A. Abubakar, “Banana leaf ash as sustainable alternative raw material for the production of concrete : a review,” Discover Materials, 2025, doi: 10.1007/s43939-025-00296-6. DOI: https://doi.org/10.1007/s43939-025-00296-6
A. Ouamane, A. Chouchane, Y. Himeur, and S. Member, “Optimized Vision Transformers for Superior Plant Disease Detection,” IEEE Access, vol. 13, no. March, pp. 48552–48570, 2025, doi: 10.1109/ACCESS.2025.3547416. DOI: https://doi.org/10.1109/ACCESS.2025.3547416
R. Sujatha, S. Krishnan, J. M. Chatterjee, and A. H. Gandomi, “Advancing plant leaf disease detection integrating machine learning and deep learning,” Scientific Reports, pp. 1–14, 2025, doi: 10.1038/s41598-024-72197-2. DOI: https://doi.org/10.1038/s41598-024-72197-2
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. DOI: https://doi.org/10.1016/j.jia.2023.11.037
J. D. Thiagarajan et al., “Analysis of banana plant health using machine learning techniques,” Scientific Reports, pp. 1–23, 2024, doi: 10.1038/s41598-024-63930-y. DOI: https://doi.org/10.1038/s41598-024-63930-y
C. A. Elinisa and N. Mduma, “Smart Agricultural Technology Mobile-Based convolutional neural network model for the early identification of banana diseases,” Smart Agricultural Technology, vol. 7, no. November 2023, p. 100423, 2024, doi: 10.1016/j.atech.2024.100423. DOI: https://doi.org/10.1016/j.atech.2024.100423
E. Science, “Deep learning model for detection and classification of banana diseases based on leaf images Deep learning model for detection and classification of banana diseases based on leaf images,” Frontier in Sustainable Agromaritime and Environmental Development Conference, 2024, doi: 10.1088/1755-1315/1359/1/012010. DOI: https://doi.org/10.1088/1755-1315/1359/1/012010
S. Muthusamy and S. P. Ramu, “IncepV3Dense : Deep Ensemble Based Average Learning Strategy for Identification of Micro-Nutrient Deficiency in Banana Crop,” IEEE Access, vol. 12, no. May, pp. 73779–73792, 2024, doi: 10.1109/ACCESS.2024.3405027. DOI: https://doi.org/10.1109/ACCESS.2024.3405027
Mafi, Md Mafiul Hasan Matin; Sifat, R.M.; Moazzam, Md. Golam Moazzam; Uddin, Mohammad Shorif (2023), “Banana Disease Recognition Dataset”, Mendeley Data, V1, doi: 10.17632/79w2n6b4kf.1
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
