Leveraging Deep Learning for Early and Accurate Pre-diction of Banana Crop Diseases: A Classification and Risk Assessment Framework

Main Article Content

Walter Ocimati
Sivalingam Elayabalan
Nancy Safari

Abstract

Banana crop diseases significantly threaten global food security and farmer livelihoods. Traditional visual inspection methods are subjective, time-consuming, and ineffective for early disease detection. This study addresses these challenges by proposing a novel framework utilizing deep learning for early and accurate prediction of banana crop diseases. Our objective is to develop a two-pronged approach: 1) disease classification using deep learning to analyze images and identify healthy or diseased plants, and 2) risk assessment incorporating weather patterns and historical disease data to predict areas susceptible to outbreaks. This combined approach offers a more comprehensive disease management strategy compared to traditional methods. We plan to utilize a dataset of banana leaf images labeled with disease status and relevant environmental data. The performance of the deep learning model will be evaluated using metrics like accuracy, precision, and recall for disease classification. This data-driven framework has the potential to revolutionize banana crop disease management by enabling early detection, targeted prevention strategies, and ultimately, increased yield, reduced economic losses for farmers, and enhanced food security.

Article Details

How to Cite
[1]
Walter Ocimati, Sivalingam Elayabalan, and Nancy Safari, “Leveraging Deep Learning for Early and Accurate Pre-diction of Banana Crop Diseases: A Classification and Risk Assessment Framework”, Int. J. Comput. Eng. Res. Trends, vol. 11, no. 4, pp. 46–57, Apr. 2024.
Section
Research Articles

References

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324

Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Con-ference on Knowledge Discovery and Data Mining (pp. 785-794). https://doi.org/10.1145/2939672.2939785

Deng, L., & Yu, D. (2014). Deep learning: Methods and applications. Foundations and Trends in Signal Processing, 7(3-4), 197-387. https://doi.org/10.1561/2000000039

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin can-cer with deep neural networks. Nature, 542(7639), 115-118. https://doi.org/10.1038/nature21056

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770-778). https://doi.org/10.1109/CVPR.2016.90

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735

Hu, W., Ma, X., Liu, G., & Li, Y. (2020). Identi-fication of grape leaf diseases based on deep convolutional neural networks. Neurocompu-ting, 383, 308-321. https://doi.org/10.1016/j.neucom.2019.11.007

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep con-volutional neural networks. In Advances in Neural Information Processing Systems (pp. 1097-1105).

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539

Li, H., Xie, P., Wang, W., & Zhao, Z. (2019). A hybrid model of deep learning and XGBoost for crop yield prediction. Agronomy, 9(5), 237. https://doi.org/10.3390/agronomy9050237

Liu, B., Zhang, Y., He, D., & Li, Y. (2017). Iden-tification of apple leaf diseases based on deep convolutional neural networks. Symmetry, 10(1), 11. https://doi.org/10.3390/sym10010011

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., ... & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533. https://doi.org/10.1038/nature14236

Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Sci-ence, 7, 1419. https://doi.org/10.3389/fpls.2016.01419

Nagi, J., Ducatelle, F., Di Caro, G. A., Cireşan, D. C., Meier, U., Giusti, A., ... & Schmidhuber, J. (2011). Max-pooling convolutional neural networks for vision-based hand gesture recog-nition. In 2011 IEEE International Conference on Signal and Image Processing Applications (pp. 342-347). https://doi.org/10.1109/ICSIPA.2011.6144164

Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345-1359. https://doi.org/10.1109/TKDE.2009.191

Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations (ICLR).

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). Going deeper with convolutions. In Proceed-ings of the IEEE Conference on Computer Vi-sion and Pattern Recognition (pp. 1-9). https://doi.org/10.1109/CVPR.2015.7298594

Xie, S., Girshick, R., Dollár, P., Tu, Z., & He, K. (2017). Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pat-tern Recognition (pp. 1492-1500). https://doi.org/10.1109/CVPR.2017.634

Zhang, Z., Cui, P., & Zhu, W. (2018). Deep learning on graphs: A survey. IEEE Transac-tions on Knowledge and Data Engineering. https://doi.org/10.1109/TKDE.2020.2981333

Zhou, Z. H., & Feng, J. (2017). Deep forest: Towards an alternative to deep neural net-works. In Proceedings of the 26th Internation-al Joint Conference on Artificial Intelligence (pp. 3553-3559). https://doi.org/10.24963/ijcai.2017/497