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AI-Driven Banana Leaf Spot Disease Detection Using Advanced Machine Learning Models
Authors
1 2 3 Department of CSA, GIET University Gunupur, Odisha, India. 4 Madanapalle Institute of Technology and Science University, Andhra Pradesh, India.
Article Information
- Organised By
- Madanapalle Institute of Technology & Science (MITS)
- Conference Date
- 9 & 10 March 2026
- Pages
- 106-112
- Article Type
- Research Article
- Check for updates
-
References
1. Silva, R., & Patel, M. “Standardizing image resolutions for CNN-based agricultural applications”. Computational Agriculture Journal, 9(2), 55-72, 2023.
2. Ahmed, T., & Kumar, S. “Economic impacts of banana diseases in tropical agriculture. Journal of Food Security Studies”, 11(1), 101-112, 2021.
3. Mishra, S. R., Dash, S., Padhy, S., Kumar, N., & Dash, Y. (2024, September). Integrating Multi-Omics Data for Advanced Diabetes Prediction and Understanding. In 2024 7th International Conference on Contemporary Computing and Informatics (IC3I) (Vol. 7, pp. 1447-1453). IEEE.
4. Dash, S., Mishra, S. R., & Baboo, A. (2025, January). Enhancing Diabetes Prediction using Hybrid Ensemble Approach. In 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN) (pp. 1-6). IEEE.
5. Williams, D., & Zhang, X. “Automated systems in disease monitoring for sustainable crop management”. International Journal of Precision Agriculture, 8(3), 142-157, 2022.
6. Lee, J., & Tran, T. “Comparing manual and automated disease detection in banana crops”. Journal of Tropical Agricultural Technology, 10(1), 32-46, 2021.
7. Dash, S., Mishra, S. R., & Baboo, A. (2025, January). Efficient Prediction of Diabetes Mellitus Through Hybrid Ensemble Machine Learning Model Using IoT. In 2025 1st International Conference on AIML-Applications for Engineering & Technology (ICAET) (pp. 1-6). IEEE.
8. Mishra, S. R., & Dash, S. (2024, December). Machine Learning Based Diabetes Prediction Using the PIMA Indian Dataset. In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) (pp. 1-6). IEEE.
9. Mishra, S. R., Dash, S., Padhy, S., & Das, R. K. (2024, September). Diabetic Foot Ulcer Diagnosis Through Deep Learning Model. In 2024 International Conference on Artificial Intelligence and Emerging Technology (Global AI Summit) (pp. 1194-1199). IEEE.
10. Rana, P., & Green, J. “Challenges in traditional crop inspection methods and future solutions”. International Journal of Agricultural Research, 15(4), 225-238, 2022.
11. Thompson, R., & Gupta, K. “Scaling machine learning models for large-scale agriculture”. Agricultural AI Journal, 16(2), 113-127, 2023.
12. Mishra, S. R., & Dash, S. (2024). Iterative Model Design for Diabetes Analysis Using FedOmics Causal Network and Federated Multi-Omics Variational Autoencoder. International Journal of Emerging Technologies and Innovative Research, 11(6).
13. Mishra, S. R., & Dash, S. (2024). Predictive Analysis On Diabetes Detection Using Pima Indian Diabetes Dataset. IJRAR-International Journal of Research and Analytical Reviews (IJRAR), 11(2).
14. Baboo, A., Mishra, S. R., & Dash, S. (2024, November). An Improvised Diabetes Prediction System Using Hybrid Ensemble Approach. In 2024 IEEE 11th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) (pp. 1-6). IEEE.
15. Chen, X., & Liu, Y. “CNN-based models for crop disease detection: A survey of recent developments”. Machine Learning in Agriculture, 12(3), 58-73, 2021.
16. Alzubaidi, F. I. A., et al., “Image Augmentation Techniques for Deep Learning: A Comprehensive Review, “ 2022.
17. Nguyen, A., & Smith, R. “Identifying disease patterns on banana leaves using CNNs”. AI in Crop Health, 4(3), 167179, 2022.
18. Alwan, A. A., et al., “Detection and Classification of Banana Leaf Diseases Using CNN,”IEEE XPLORE, 2022.
19. M. A. B. Bhuiyan, H. M. Abdullah, S. E. Arman, S. S. Rahman, and
20. K. A. Mahmud, "BananaSqueezeNet: A very fast, lightweight convolutional neural network for the diagnosis of three prominent banana leaf diseases," “Smart Agricultural Technology”, vol. 4, pp. 100214, 2023.
21. Rath, L., Mishra, S. R., Dash, S., Pradhan, P. C., & Baboo, A. (2025). Predicting diabetic patients coronary artery calcium score, deep learning using retinal images. In Intelligent Computing Techniques and Applications (pp. 113-118). CRC Press.
22. Kumar, N., & Tran, Q. “Real-world applications of convolutional neural networks in agriculture” Journal of Agricultural AI, 5(1), 102-119, 2024.
23. Mishra, S. R., & Dash, S. (2026). AI - Driven Remote Health Monitoring for Predicting Diabetes and Heart Diseases Using ULMCSO and PGND Models. Hyper - Intelligent Networks: Exploring the Future of Connectivity for Society 5.0, 219-247.
24. Mishra, S. R., Dash, S., Padhy, S., & Samuel, P. (2026). Legal Aspects of Operating IoMT Applications in the Fog Computing. In Integrating Cloud, Fog, and Edge Computing in Healthcare: Federated Learning and Blockchain Approaches: Harnessing Distributed Technologies for Enhanced Healthcare Delivery (pp. 211-224). Cham: Springer Nature Switzerland.
25. BananaLSD Dataset M. A. B. Bhuiyan, H. M. Abdullah, S. E. Arman, S. S. Rahman, and K. A. Mahmud, "BananaSqueezeNet: A very fast, lightweight convolutional neural network for the diagnosis of three prominent banana leaf diseases," *Smart Agricultural Technology*, vol. 4, pp. 100214, 2023.
26. Pattnayak, A. P., Mishra, S. R., Dash, S., & Baboo, A. (2025). Utilization of deep learning and machine learning models to approach high glucose and low glucose prediction with type 1 diabetes mellitus in adult patients. In Intelligent Computing Techniques and Applications (pp. 102-107). CRC Press.
27. Dora, N., Dash, S., Baboo, A., & Mishra, S. R. (2025, August). Efficient Nail Disease Diagnosis Using Deep Neural Networks for Predicting Abnormalities. In 2025 International Conference on Next Generation of Green Information and Emerging Technologies (GIET) (pp. 1-5). IEEE.
28. Mishra, S. R., Dash, S., & Rath, L. (2024, November). Effective Diabetes Mellitus Prediction Using a Hybrid Ensemble Machine Learning Model with Iot. In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS) (pp. 1-8). IEEE.
29. Matplotlib Documentation. “Visualizing data in machine learning research". Retrieved from https://matplotlib.org , 2022.
30. Khuntuli, B., Dash, S., Pradhan, P. C., & Mishra, S. R. Combating food insecurity through remote sensing and machine learning for enhanced crop yield prediction. In Intelligent Computing Techniques and Applications (pp. 135-140). CRC Press.
31. Zhang, H., et al., “An Improved Gabor Feature Extraction Method for Plant Disease Detection,” 2022.
32. Baboo, A., Dash, S., Padhy, S., & Samuel, P. (2026). Testing Perspectives of Fog-Based IoMT Applications with Federated Learning. In Integrating Cloud, Fog, and Edge Computing in Healthcare: Federated Learning and Blockchain Approaches: Harnessing Distributed Technologies for Enhanced Healthcare Delivery (pp. 199-210). Cham: Springer Nature Switzerland.
33. Sahu, P. K., Biswal, B. B., Mishra, S. R., Padhy, J., & Kumar, D. (2025, March). Demand-Based Secured Data Transmission in WSN. In International Conference on Next Generation Computing and Communication Applications (pp. 37-44). Cham: Springer Nature Switzerland.
34. Baboo, A., Patro, S. P., & Dash, S. (2024, December). A Deep Learning Approach for Enhancing Cardiovascular Disease Prediction Using ECG Data. In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) (pp. 1-5). IEEE.
35. Dash, A. B., Dash, S., Padhy, S., Kumar, N., Pati, G. K., & Uthansingh, K. (2025). Leveraging inception-v3 CNN model for efficient image classification. In Intelligent Computing and Communication Techniques (pp. 341-348). CRC Press.
36. Dash, A. B., Dash, S., Padhy, S., Mishra, B., & Paikaray, B. K. (2025). Streamlining colorectal cancer diagnosis: leveraging MobileNet-V3 for efficient image classification. Int. J. Internet Manufacturing and Services, 11(4), 317.
37. Dash, S., Padhy, S., Kumar, N., & Nayyar, A. (2026). Medisecure: a hybrid approach for enhancing multimedia data protection in healthcare. Cluster Computing, 29(1), 75.
38. Dash, S., Padhy, S., Suman, P., Mal, S., Malviya, L., Suman, A., & Kishore, J. (2025). Privacy-preserving diabetes and heart disease prediction via federated learning and WCO. International Journal of Computational Intelligence Systems, 18(1), 217
39. Khandakhani, S. W., Dash, S., Padhy, S., & Panda, R. (2024, December). Implementation of Customized Convolutional Neural Networks for Handwritten Marathi Character Recognition. In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) (pp. 1-6). IEEE.
40. Kumar, K. D., Dash, S., Ganiya, R. K., & Pradhan, P. C. (2025). A genetic algorithm-optimized hybrid CNN-LSTM for robust EEG seizure detection and adversarial defense. In Intelligent Computing Techniques and Applications (pp. 96-101). CRC Press.
41. Paithankar, O. N., Haider, A. M., Suman, P., Padhy, S., Siddiqui, M. H., & Dash, S. (2024, September). Intrusion Detection Systems to avoid Cyberattack using Machine Learning. In 2024 7th International Conference on Contemporary Computing and Informatics (IC3I) (Vol. 7, pp. 1440-1446). IEEE.
2. Ahmed, T., & Kumar, S. “Economic impacts of banana diseases in tropical agriculture. Journal of Food Security Studies”, 11(1), 101-112, 2021.
3. Mishra, S. R., Dash, S., Padhy, S., Kumar, N., & Dash, Y. (2024, September). Integrating Multi-Omics Data for Advanced Diabetes Prediction and Understanding. In 2024 7th International Conference on Contemporary Computing and Informatics (IC3I) (Vol. 7, pp. 1447-1453). IEEE.
4. Dash, S., Mishra, S. R., & Baboo, A. (2025, January). Enhancing Diabetes Prediction using Hybrid Ensemble Approach. In 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN) (pp. 1-6). IEEE.
5. Williams, D., & Zhang, X. “Automated systems in disease monitoring for sustainable crop management”. International Journal of Precision Agriculture, 8(3), 142-157, 2022.
6. Lee, J., & Tran, T. “Comparing manual and automated disease detection in banana crops”. Journal of Tropical Agricultural Technology, 10(1), 32-46, 2021.
7. Dash, S., Mishra, S. R., & Baboo, A. (2025, January). Efficient Prediction of Diabetes Mellitus Through Hybrid Ensemble Machine Learning Model Using IoT. In 2025 1st International Conference on AIML-Applications for Engineering & Technology (ICAET) (pp. 1-6). IEEE.
8. Mishra, S. R., & Dash, S. (2024, December). Machine Learning Based Diabetes Prediction Using the PIMA Indian Dataset. In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) (pp. 1-6). IEEE.
9. Mishra, S. R., Dash, S., Padhy, S., & Das, R. K. (2024, September). Diabetic Foot Ulcer Diagnosis Through Deep Learning Model. In 2024 International Conference on Artificial Intelligence and Emerging Technology (Global AI Summit) (pp. 1194-1199). IEEE.
10. Rana, P., & Green, J. “Challenges in traditional crop inspection methods and future solutions”. International Journal of Agricultural Research, 15(4), 225-238, 2022.
11. Thompson, R., & Gupta, K. “Scaling machine learning models for large-scale agriculture”. Agricultural AI Journal, 16(2), 113-127, 2023.
12. Mishra, S. R., & Dash, S. (2024). Iterative Model Design for Diabetes Analysis Using FedOmics Causal Network and Federated Multi-Omics Variational Autoencoder. International Journal of Emerging Technologies and Innovative Research, 11(6).
13. Mishra, S. R., & Dash, S. (2024). Predictive Analysis On Diabetes Detection Using Pima Indian Diabetes Dataset. IJRAR-International Journal of Research and Analytical Reviews (IJRAR), 11(2).
14. Baboo, A., Mishra, S. R., & Dash, S. (2024, November). An Improvised Diabetes Prediction System Using Hybrid Ensemble Approach. In 2024 IEEE 11th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) (pp. 1-6). IEEE.
15. Chen, X., & Liu, Y. “CNN-based models for crop disease detection: A survey of recent developments”. Machine Learning in Agriculture, 12(3), 58-73, 2021.
16. Alzubaidi, F. I. A., et al., “Image Augmentation Techniques for Deep Learning: A Comprehensive Review, “ 2022.
17. Nguyen, A., & Smith, R. “Identifying disease patterns on banana leaves using CNNs”. AI in Crop Health, 4(3), 167179, 2022.
18. Alwan, A. A., et al., “Detection and Classification of Banana Leaf Diseases Using CNN,”IEEE XPLORE, 2022.
19. M. A. B. Bhuiyan, H. M. Abdullah, S. E. Arman, S. S. Rahman, and
20. K. A. Mahmud, "BananaSqueezeNet: A very fast, lightweight convolutional neural network for the diagnosis of three prominent banana leaf diseases," “Smart Agricultural Technology”, vol. 4, pp. 100214, 2023.
21. Rath, L., Mishra, S. R., Dash, S., Pradhan, P. C., & Baboo, A. (2025). Predicting diabetic patients coronary artery calcium score, deep learning using retinal images. In Intelligent Computing Techniques and Applications (pp. 113-118). CRC Press.
22. Kumar, N., & Tran, Q. “Real-world applications of convolutional neural networks in agriculture” Journal of Agricultural AI, 5(1), 102-119, 2024.
23. Mishra, S. R., & Dash, S. (2026). AI - Driven Remote Health Monitoring for Predicting Diabetes and Heart Diseases Using ULMCSO and PGND Models. Hyper - Intelligent Networks: Exploring the Future of Connectivity for Society 5.0, 219-247.
24. Mishra, S. R., Dash, S., Padhy, S., & Samuel, P. (2026). Legal Aspects of Operating IoMT Applications in the Fog Computing. In Integrating Cloud, Fog, and Edge Computing in Healthcare: Federated Learning and Blockchain Approaches: Harnessing Distributed Technologies for Enhanced Healthcare Delivery (pp. 211-224). Cham: Springer Nature Switzerland.
25. BananaLSD Dataset M. A. B. Bhuiyan, H. M. Abdullah, S. E. Arman, S. S. Rahman, and K. A. Mahmud, "BananaSqueezeNet: A very fast, lightweight convolutional neural network for the diagnosis of three prominent banana leaf diseases," *Smart Agricultural Technology*, vol. 4, pp. 100214, 2023.
26. Pattnayak, A. P., Mishra, S. R., Dash, S., & Baboo, A. (2025). Utilization of deep learning and machine learning models to approach high glucose and low glucose prediction with type 1 diabetes mellitus in adult patients. In Intelligent Computing Techniques and Applications (pp. 102-107). CRC Press.
27. Dora, N., Dash, S., Baboo, A., & Mishra, S. R. (2025, August). Efficient Nail Disease Diagnosis Using Deep Neural Networks for Predicting Abnormalities. In 2025 International Conference on Next Generation of Green Information and Emerging Technologies (GIET) (pp. 1-5). IEEE.
28. Mishra, S. R., Dash, S., & Rath, L. (2024, November). Effective Diabetes Mellitus Prediction Using a Hybrid Ensemble Machine Learning Model with Iot. In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS) (pp. 1-8). IEEE.
29. Matplotlib Documentation. “Visualizing data in machine learning research". Retrieved from https://matplotlib.org , 2022.
30. Khuntuli, B., Dash, S., Pradhan, P. C., & Mishra, S. R. Combating food insecurity through remote sensing and machine learning for enhanced crop yield prediction. In Intelligent Computing Techniques and Applications (pp. 135-140). CRC Press.
31. Zhang, H., et al., “An Improved Gabor Feature Extraction Method for Plant Disease Detection,” 2022.
32. Baboo, A., Dash, S., Padhy, S., & Samuel, P. (2026). Testing Perspectives of Fog-Based IoMT Applications with Federated Learning. In Integrating Cloud, Fog, and Edge Computing in Healthcare: Federated Learning and Blockchain Approaches: Harnessing Distributed Technologies for Enhanced Healthcare Delivery (pp. 199-210). Cham: Springer Nature Switzerland.
33. Sahu, P. K., Biswal, B. B., Mishra, S. R., Padhy, J., & Kumar, D. (2025, March). Demand-Based Secured Data Transmission in WSN. In International Conference on Next Generation Computing and Communication Applications (pp. 37-44). Cham: Springer Nature Switzerland.
34. Baboo, A., Patro, S. P., & Dash, S. (2024, December). A Deep Learning Approach for Enhancing Cardiovascular Disease Prediction Using ECG Data. In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) (pp. 1-5). IEEE.
35. Dash, A. B., Dash, S., Padhy, S., Kumar, N., Pati, G. K., & Uthansingh, K. (2025). Leveraging inception-v3 CNN model for efficient image classification. In Intelligent Computing and Communication Techniques (pp. 341-348). CRC Press.
36. Dash, A. B., Dash, S., Padhy, S., Mishra, B., & Paikaray, B. K. (2025). Streamlining colorectal cancer diagnosis: leveraging MobileNet-V3 for efficient image classification. Int. J. Internet Manufacturing and Services, 11(4), 317.
37. Dash, S., Padhy, S., Kumar, N., & Nayyar, A. (2026). Medisecure: a hybrid approach for enhancing multimedia data protection in healthcare. Cluster Computing, 29(1), 75.
38. Dash, S., Padhy, S., Suman, P., Mal, S., Malviya, L., Suman, A., & Kishore, J. (2025). Privacy-preserving diabetes and heart disease prediction via federated learning and WCO. International Journal of Computational Intelligence Systems, 18(1), 217
39. Khandakhani, S. W., Dash, S., Padhy, S., & Panda, R. (2024, December). Implementation of Customized Convolutional Neural Networks for Handwritten Marathi Character Recognition. In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) (pp. 1-6). IEEE.
40. Kumar, K. D., Dash, S., Ganiya, R. K., & Pradhan, P. C. (2025). A genetic algorithm-optimized hybrid CNN-LSTM for robust EEG seizure detection and adversarial defense. In Intelligent Computing Techniques and Applications (pp. 96-101). CRC Press.
41. Paithankar, O. N., Haider, A. M., Suman, P., Padhy, S., Siddiqui, M. H., & Dash, S. (2024, September). Intrusion Detection Systems to avoid Cyberattack using Machine Learning. In 2024 7th International Conference on Contemporary Computing and Informatics (IC3I) (Vol. 7, pp. 1440-1446). IEEE.
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