Data availability The WSIs, nephrographic CT scans, and annotation data used for both the training and validation sets are subject to institutional restrictions. Due to patient privacy obligations and Institutional Review Board (IRB) approvals, these data are not publicly available. However, they can be accessed upon reasonable request from the corresponding author, pending approval from
Attention enhanced hybrid deep learning architecture with PCA-based feature fusion for banana leaf disease detection – Scientific Reports
References
-
Awuchi, C. G. HACCP, quality, and food safety management in food and agricultural systems. Cogent Food Agric. 9 (1), 2176280. https://doi.org/10.1080/23311932.2023.2176280 (2023).
Google Scholar
-
Rashid, J., Khan, I., Ali, G., Rehman, S. U. & Alturise, F. Real-time multiple guava leaf disease detection from a single leaf using hybrid deep learning technique. Comput. Mater. Contin. 74 (1), 1235–1257 (2023).
Google Scholar
-
Raja, N. B. & Selvi Rajendran, P. An efficient banana plant leaf disease classification using optimal ensemble deep transfer network. J. Exp. Theor. Artif. Intell. 37 (4), 585–608. https://doi.org/10.1080/0952813X.2023.2241867 (2025).
Google Scholar
-
Yumang, A. N., Baguisi, J. M., Buenaventura, B. R. S. & Paglinawan, C. C. Detection of Black Sigatoka Disease on Banana Leaves Using ShuffleNet V2 CNN Architecture in Comparison to SVM and KNN Techniques, in 15th International Conference on Computer and Automation Engineering (ICCAE), 281–286. https://doi.org/10.1109/ICCAE56788.2023.10111367 (2023).
-
Madhusudhan, C. K., Mahendra, K., Raghavendra, N., Revanasiddappa, M. & Faisal, M. Corrosion-resistant polypyrrole-banana carbon (PPy-BC) nanocomposites for protection against electromagnetic interference: a green approach. J. Mater. Sci. Mater. Electron. 33 (3), 1366–1382. https://doi.org/10.1007/s10854-021-07466-1 (2022)
Google Scholar
-
Arslan, M. et al. A deep features based approach using modified ResNet50 and gradient boosting for visual sentiments classification, in IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR), 239–242. https://doi.org/10.1109/MIPR62202.2024.00045 (2024).
-
Ahmed, M. & Ahmed, A. Palm tree disease detection and classification using residual network and transfer learning of inception ResNet. PLOS ONE. 18 (3), e0282250. https://doi.org/10.1371/journal.pone.0282250 (2023).
Google Scholar
-
Akram, A., Tariq, A., Ali, M. S., Tariq, M. U. & Altaf, A. Recognizing facial expressions across cultures using gradient features. J. Innov. Comput. Emerg. Technol. 3, 1 (2023).
Google Scholar
-
Huang, Y. et al. VGG16-AttnNet: A banana leaf lesion recognition model combining VGG16 and self-attention mechanism, in 5th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE), S745–748. https://doi.org/10.1109/ICBASE63199.2024.10762417 (2024).
-
Saini, A. Efficient banana leaf disease classification using Vision Transformer (ViT) model, in 4th International Conference on Technological Advancements in Computational Sciences (ICTACS), 176–180. https://doi.org/10.1109/ICTACS62700.2024.10841002 (2024).
-
Sahu, P., Singh, A. P., Chug, A. & Singh, D. A Systematic literature review of machine learning techniques deployed in agriculture: A case study of banana crop. IEEE Access 10, 87333–87360. https://doi.org/10.1109/ACCESS.2022.3199926 (2022).
Google Scholar
-
Chong, M. Z., Taujuddin, N. S. A. M., Sari, S., Tukiran, Z. & Ghani, A. R. A. Banana leaf disease classification using SqueezeNet, AlexNet and MobileNet. J. Electron. Volt Appl. 6 (1), 48–58 (2025).
Google Scholar
-
Rajalakshmi, N. R. et al. Early detection of banana leaf disease using novel deep convolutional neural network. J. Data Sci. Intell. Syst. 3 (3), 192–199. https://doi.org/10.47852/bonviewJDSIS42021530 (2025).
Google Scholar
-
Arun, A., Sharma, S., Singh, B. & Hazra, T. Identification of plant species using convolutional neural network with transfer learning. J. Phytopathol. 173 (1), e70032. https://doi.org/10.1111/jph.70032 (2025).
Google Scholar
-
Yan, K., Shisher, M. K. C. & Sun, Y. A. Transfer learning-based deep convolutional neural network for detection of fusarium wilt in banana crops. AgriEngineering 5 (4), 2381–2394 https://doi.org/10.3390/agriengineering5040146 (2023).
Google Scholar
-
Nagachandrika, B., Prasath, R. & Praveen Joe, I. R. An automatic classification framework for identifying type of plant leaf diseases using multi-scale feature fusion-based adaptive deep network. Biomed. Signal. Process. Control. 95, 106316. https://doi.org/10.1016/j.bspc.2024.106316 (2024).
-
Pham, T. C., Nguyen, T. N. & Nguyen, V. D. Ambiguity-aware semi-supervised learning for leaf disease classification. Sci. Rep. 15 (1), 14070. https://doi.org/10.1038/s41598-025-95849-3 (2025).
Google Scholar
-
Saraswathi, M. S. et al. Production of quality planting material in commercial banana cvs. Rasthali (Silk, AAB) and Neypoovan (Neypoovan, AB) through farmer-friendly macropropagation technique and their field evaluation. South. Afr. J. Bot. 167, 410–418. https://doi.org/10.1016/j.sajb.2024.02.047 (2024).
Google Scholar
-
Nagu, B., Kaur, G. & Shanmugavelu, M. & MRM, V. Prediction of banana leaf disease by intelligent algorithm through digital image using neural network technique. Multimed. Tools Appl. 84 (28), 34249–34265. https://doi.org/10.1007/s11042-024-20529-9 (2025).
Google Scholar
-
Yan, W., Feng, Q., Yang, S., Zhang, J. & Yang, W. Heterogeneous metric fusion network-based few-shot learning for crop disease recognition. Agronomy 13 (12), 2876. https://doi.org/10.3390/agronomy13122876 (2023).
Google Scholar
-
Thirumeninathan, V., Vijayalakshmi, S. & Palathara, S. T. Enhancing banana cultivation: Disease identification through CNN and SVM analysis for optimal plant health, in International Conference on Trends in Quantum Computing and Emerging Business Technologies, 1–6. https://doi.org/10.1109/TQCEBT59414.2024.10545264 (2024).
-
Saini, A. Efficient banana leaf disease classification using vision transformer (ViT) model, in 4th International Conference on Technological Advancements in Computational Sciences (ICTACS), 176–180. https://doi.org/10.1109/ICTACS62700.2024.10841002 (2024).
-
Rameshkumar, R., Arunkumar, G., Devi, B. A. & Pandi, S. S. Enhanced plant disease classification via wild horse optimizer and convolutional attention-bidirectional long short term memory. Signal Image Video Process. 19 (12), 1010. https://doi.org/10.1007/s11760-025-04556-z (2025).
Google Scholar
-
Senthil Pandi, S., Reshmy, A. K., Muruganandam, S. & Manju, I. Hybrid Crossover oppositional firefly optimization for enhanced deep transfer learning in plant leaf disease classification. J. Crop Health. 77 (5), 146. https://doi.org/10.1007/s10343-025-01205-w (2025).
Google Scholar
-
Sellam, V., Kannan, N., Pandi, S., Manju, I. & S. & Enhancing sustainable agriculture using attention convolutional bidirectional Gated recurrent based modified leaf in wind algorithm: Integrating AI and IoT for efficient farming. Sustain. Comput. Inf. Syst. 47, 101160. https://doi.org/10.1016/j.suscom.2025.101160 (2025).
Google Scholar
-
S, S. P., Pounambal, M., Vellingiri, J. G. J. & K, A. Advancing agricultural sustainability with gradient weighted Densenet-201 model for accurate detection of plant leaf diseases. Environ. Dev. Sustain. https://doi.org/10.1007/s10668-025-06314-0 (2025).
Google Scholar
-
Senthil Pandi, S., Reshmy, A. K., Elangovan, D. & Vellingiri, J. AI-driven environmental monitoring for hydroponic agriculture: ExCNN-LFCP approach. Earth Sci. Inf. 18 (1), 73. https://doi.org/10.1007/s12145-024-01516-y (2024).
Google Scholar
-
Mathew, D., Kumar, C. S. & Cherian, K. A. Integration of nondecimated quaternion wavelet transform and neighborhood texture patterns for disease classification in banana (Musa spp.) foliage. Multimed. Tools Appl. 82 (24), 37327–37349 https://doi.org/10.1007/s11042-023-14869-1 (2023).
Google Scholar
-
Rehman, A. et al. An intelligent deep augmented model for detection of banana leaves diseases. Microsc Res. Tech. 88 (1), 53–64. https://doi.org/10.1002/jemt.24681 (2025).
Google Scholar
-
Das, U., Azam, S. & Al Kafi, M. A. Banana and banana leaf dataset for classification and disease detection, 2 https://doi.org/10.17632/5nfjzntwd8.2 (2025).
-
Mafi, M. M. H. M., Sifat, R. M., Moazzam, M. G. M. & Uddin, M. S. Banana disease recognition dataset. Mendeley Data https://doi.org/10.17632/79w2n6b4kf.1 (2023).
-
Arman, S. E., Baki Bhuiyan, M. A., Abdullah, H. M., Islam, S., Chowdhury, T. T. & Hossain, M. A. Banana leaf spot diseases (BananaLSD) dataset for classification of banana leaf diseases using machine learning. Data Brief 50 109608. https://doi.org/10.17632/9tb7k297ff.1 (2023).
Google Scholar
-
Haq, M. K. et al. Enhancing banana leaf spot disease classification using dense Mobilenet V2, in International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), 1–6. https://doi.org/10.1109/ACCAI61061.2024.10602228 (2024).
-
Shu, Y., Zhang, J., Wang, Y. & Wei, Y. Fruit freshness classification and detection based on the ResNet-101 network and non-local attention mechanism. Foods 14 (11), 1987 https://doi.org/10.3390/foods14111987 (2025).
-
Akram, A. et al. Recognizing breast cancer using edge-weighted texture features of histopathology images. Comput. Mater. Contin. 77 (1), 1081–1101 https://doi.org/10.32604/cmc.2023.041558 (2023).
Google Scholar
-
Akram, A., Rashid, J., Jaffar, A., Hajjej, F., Iqbal, W. & Sarwar, N. Weber law based approach for multi-class image forgery detection. Comput. Mater. Contin. 78 (1), 145–166. https://doi.org/10.32604/cmc.2023.041074 (2024).
Google Scholar
-
Deng, J., Huang, W., Zhou, G., Hu, Y., Li, L. & Wang, Y. Identification of banana leaf disease based on KVA and GR-ARNet. J. Integr. Agric. 23 (10), 3554–3575 https://doi.org/10.1016/j.jia.2023.11.037 (2024).
Google Scholar
-
Shukla, A. N., Joshi, K., Singh Yadav, A. P., Kukreja, V. & Mehta, S. Precision phytopathology in agriculture: A federated learning CNN framework for banana leaf disease classification, in 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 1–7 https://doi.org/10.1109/ICRITO61523.2024.10522433 (2024).
-
Sum, A. S. I., Saha, A. K., Nur, M. K. & Hasan, K. T. BananaLeaf-Net: A proposed deep learning model for accurate classification of banana leaf diseases, in Machine vision in plant leaf disease detection for sustainable agriculture (eds Mridha, M. F. & Dey, N.) 41–56 (Springer Nature, 2025).
Google Scholar
-
Ancheta, J. B., Santos, L. Q., De Jesus, L. C. M., David, J. V., Avelino, A. M. & Grande, M. E. A. Development of a classification model for banana leaf disease using google teachable machine, in International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 0258–0263. https://doi.org/10.1109/ICAIIC64266.2025.10920850 (2025).
-
Singla, S., Jagadish, S. S., Saxena, M., Mahajan, S., Singh, A. R. & Gupta, R. Automated detection of banana leaf diseases using ResNet50, in 6th International Conference for Emerging Technology (INCET) 1–5 https://doi.org/10.1109/INCET64471.2025.11140293 (2025).
-
Jiménez, N., Orellana, S., Mazon-Olivo, B., Rivas-Asanza, W. & Ramírez-Morales, I. Detection of leaf diseases in banana crops using deep learning techniques. AI 6 (3), 61. https://doi.org/10.3390/ai6030061 (2025).
Download references
