Banana Leaf Disease Prediction Using Machine Learning and Deep Learning Techniques
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Abstract
Banana cultivation plays a critical role in global agriculture; however, its productivity is severely affected by various foliar diseases such as Sigatoka, Panama wilt and Banana Bunchy Top Disease (BBTD). Early detection of these diseases is essential to minimize yield loss and ensure sustainable agricultural practices. Traditional diagnostic approaches rely on manual inspection, which is often subjective, time-consuming and inaccessible to farmers in remote regions.
This study presents a robust and automated framework for banana leaf disease prediction using both Machine Learning (ML) and Deep Learning (DL) techniques. The proposed system employs image-based analysis, incorporating preprocessing methods such as normalization, resizing and data augmentation. Classical ML models including Support Vector Machine (SVM), k-Nearest Neighbors (KNN)and Random Forest are compared with deep learning architectures such as Convolutional Neural Networks (CNN) and transfer learning-based ResNet50.
Experimental results demonstrate that deep learning models significantly outperform traditional ML approaches, achieving a maximum accuracy of 97.3% using ResNet50. The findings highlight the potential of AI-driven systems in enabling early disease detection and supporting precision agriculture.
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[1] S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrkand D. Stefanovic, “Deep neural networks based recognition of plant diseases by leaf image classification,” Computational Intelligence and Neuroscience, vol. 2016, pp. 1–11, 2016. DOI: https://doi.org/10.1155/2016/3289801
[2] P. Mohanty, D. Hughesand M. Salathé, “Using deep learning for image-based plant disease detection,” Frontiers in Plant Science, vol. 7, p. 1419, 2016. DOI: https://doi.org/10.3389/fpls.2016.01419
[3] K. P. Ferentinos, “Deep learning models for plant disease detection and diagnosis,” Computers and Electronics in Agriculture, vol. 145, DOI: https://doi.org/10.1016/j.compag.2018.01.009
pp. 311–318, 2018.
[4] S. Barbedo, “Factors influencing the use of deep learning for plant disease recognition,” Biosystems Engineering, vol. 172, pp. 84–91, 2018. DOI: https://doi.org/10.1016/j.biosystemseng.2018.05.013
[5] A. Kamilaris and F. X. Prenafeta-Boldú, “Deep learning in agriculture: A survey,” Computers and Electronics in Agriculture, vol. 147, pp. 70–90, 2018. DOI: https://doi.org/10.1016/j.compag.2018.02.016
[6] J. Too, A. Yujian, S. Njukiand L. Yingchun, “A comparative study of fine-tuning deep learning models for plant disease identification,” Computers and Electronics in Agriculture, vol. 161, pp. 272–279, 2019. DOI: https://doi.org/10.1016/j.compag.2018.03.032
[7] M. Hasan, M. Tanawalaand V. Patel, “Deep learning precision farming: Tomato leaf disease detection by transfer learning,” Procedia Computer Science, vol. 167, pp. 154–161, 2020. DOI: https://doi.org/10.2139/ssrn.3349597
[8] A. Brahimi, K. Boukhalfaand A. Moussaoui, “Deep learning for tomato diseases: Classification and symptoms visualization,” Applied Artificial Intelligence, vol. 31, no. 4, pp. 299–315, 2017. DOI: https://doi.org/10.1080/08839514.2017.1315516
[9] S. Ramesh and D. Vydeki, “Recognition and classification of paddy leaf diseases using optimized deep neural network,” Artificial Intelligence in Agriculture, vol. 2, pp. 1–9, 2019. DOI: https://doi.org/10.1016/j.inpa.2019.09.002
[10] H. Durmuş, E. O. Güneşand M. Kırcı, “Disease detection on the leaves of the tomato plants by using deep learning,” in Proc. IEEE Int. Conf. Agro-Geoinformatics, 2017, pp. 1–5. DOI: https://doi.org/10.1109/Agro-Geoinformatics.2017.8047016
[11] M. Turkoglu and D. Hanbay, “Plant disease and pest detection using deep learning-based features,” Turkish Journal of Electrical Engineering & Computer Sciences, vol. 27, no. 3, pp. 1636–1651, 2019. DOI: https://doi.org/10.3906/elk-1809-181
[12] S. Zhang, S. Huangand C. Zhang, “Plant disease detection using convolutional neural networks,” Journal of Electrical and Computer Engineering, vol. 2018, pp. 1–9, 2018. DOI: https://doi.org/10.1155/2018/9373210
[13] R. Picon et al., “Deep convolutional neural networks for mobile capture device-based crop disease classification,” Computers and Electronics in Agriculture, vol. 161, pp. 280–290, 2019. DOI: https://doi.org/10.1016/j.compag.2018.04.002
[14] Y. LeCun, Y. Bengioand G. Hinton, “Deep learning,” Nature, vol. 521, pp. 436–444, 2015. DOI: https://doi.org/10.1038/nature14539
[15] A. Krizhevsky, I. Sutskeverand G. Hinton, “ImageNet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems (NIPS), 2012, pp. 1097–1105.
[16] K. He, X. Zhang, S. Renand J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE CVPR, 2016, pp. 770–778. DOI: https://doi.org/10.1109/CVPR.2016.90
[17] M. Sandler et al., “MobileNetV2: Inverted residuals and linear bottlenecks,” in Proc. IEEE CVPR, 2018, pp. 4510–4520. DOI: https://doi.org/10.1109/CVPR.2018.00474
[18] M. Tan and Q. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” in Proc. ICML, 2019.
[19] C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, pp. 273–297, 1995. DOI: https://doi.org/10.1023/A:1022627411411
[20] L. Breiman, “Random forests,” Machine Learning, vol. 45, pp. 5–32, 2001. DOI: https://doi.org/10.1023/A:1010933404324
[21] T. Cover and P. Hart, “Nearest neighbor pattern classification,” IEEE Transactions on Information Theory, vol. 13, no. 1, pp. 21–27, 1967. DOI: https://doi.org/10.1109/TIT.1967.1053964
[22] R. Haralick, K. Shanmugamand I. Dinstein, “Textural features for image classification,” IEEE Transactions on Systems, Manand Cybernetics, vol. SMC-3, no. 6, pp. 610–621, 1973. DOI: https://doi.org/10.1109/TSMC.1973.4309314
[23] J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Networks, vol. 61, pp. 85–117, 2015. DOI: https://doi.org/10.1016/j.neunet.2014.09.003
[24] FAO, “Banana market review: Preliminary results,” Food and Agriculture Organization of the United Nations, 2022.
[25] A. Singh, S. Ganapathysubramanian, A. Sarkarand A. Singh, “Deep learning for plant stress phenotyping: Trends and future perspectives,” Trends in Plant Science, vol. 23, no. 10, pp. 883–898, 2018. DOI: https://doi.org/10.1016/j.tplants.2018.07.004
[26] S. Liu et al., “Plant disease detection using deep learning: A review,” Plant Methods, vol. 17, no. 1, pp. 1–18, 2021. DOI: https://doi.org/10.1186/s13007-021-00722-9
[27] R. Karthik, S. Hariharan, A. Anandand P. Mathikshara, “Attention embedded residual CNN for disease detection in tomato leaves,” Applied Soft Computing, vol. 86, 2020. DOI: https://doi.org/10.1016/j.asoc.2019.105933
[28] S. Chen, C. Zhangand Z. Dong, “Using ranking-CNN for age estimation,” in Proc. IEEE CVPR, 2017 (adapted for image classification methodology relevance). DOI: https://doi.org/10.1109/CVPR.2017.86
[29] J. Brownlee, Deep Learning for Computer Vision, Machine Learning Mastery, 2019.
[30] D. Hughes and M. Salathé, “An open access repository of images on plant health to enable the development of mobile disease diagnostics,” arXiv preprint arXiv:1511.08060, 2015.