Evaluating the Potential of Federated Learning for Maize Leaf Disease Prediction
The diagnosis of diseases in food crops based on machine learning seemed satisfactory and suitable for use on a large scale. The Convolutional Neural Networks (CNNs) perform accurately in the disease prediction considering the image capture of the crop leaf, being extensively enhanced in the literature. These machine learning techniques fall short in data privacy, as they require sharing the data in the training process with a central server, disregarding competitive or regulatory concerns. Thus, Federated Learning (FL) aims to support distributed training to address recognized gaps in centralized training. As far as we know, this paper inaugurates the use and evaluation of FL applied in maize leaf diseases. We evaluated the performance of five CNNs trained under the distributed paradigm and measured their training time compared to the classification performance. In addition, we consider the suitability of distributed training considering the volume of network traffic and the number of parameters of each CNN. Our results indicate that FL potentially enhances data privacy in heterogeneous domains.
Ahila Priyadharshini, R., Arivazhagan, S., Arun, M., and Mirnalini, A. (2019). Maize leaf disease classification using deep convolutional neural networks. Neural Computing and Applications, 31(12):8887-8895.
Alehegn, E. (2019). Ethiopian maize diseases recognition and classification using support vector machine. International Journal of Computational Vision and Robotics, 9(1):90-109.
Amin, H., Darwish, A., Hassanien, A. E., and Soliman, M. (2022). End-to-End Deep Learning Model for Corn Leaf Disease Classification. IEEE Access, 10:31103-31115.
Aragão, A. and Contini, E. (2021). O agro no Brasil e no Mundo: uma síntese do período de 2000 a 2020. Embrapa SIRE.
Ayoub Shaikh, T., Rasool, T., and Rasheed Lone, F. (2022). Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. Computers and Electronics in Agriculture, 198:107119.
Bochie, K., Sammarco, M., Detyniecki, M., and Campista, M. (2021). Análise do Aprendizado Federado em Redes Móveis. In Anais do XXXIX Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 71-84, Porto Alegre, RS, Brasil. SBC.
Brooks, P. (2001). Ethernet/IP-industrial protocol. In ETFA 2001. 8th International Conference on Emerging Technologies and Factory Automation. Proceedings (Cat. No. 01TH8597), volume 2, pages 505-514. IEEE.
da Rocha, E. L., Rodrigues, L., and Mari, J. F. (2020). Maize leaf disease classification using convolutional neural networks and hyperparameter optimization. In Anais do XVI Workshop de Visão Computacional, pages 104-110, Porto Alegre, RS, Brasil. SBC.
Dayan, I. and et al. (2021). Federated learning for predicting clinical outcomes in patients with COVID-19. Nature Medicine, 27(10):1735-1743.
DeChant, C., Wiesner-Hanks, T., Chen, S., Stewart, E. L., Yosinski, J., Gore, M. A., Nelson, R. J., and Lipson, H. (2017). Automated Identification of Northern Leaf Blight-Infected Maize Plants from Field Imagery Using Deep Learning. Phytopathology®, 107(11):1426-1432. PMID: 28653579.
Haque, M. A., Marwaha, S., Deb, C. K., Nigam, S., Arora, A., Hooda, K. S., Soujanya, P. L., Aggarwal, S. K., Lall, B., Kumar, M., Islam, S., Panwar, M., Kumar, P., and Agrawal, R. C. (2022). Deep learning-based approach for identification of diseases of maize crop. Scientific Reports, 12(1):6334.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770-778.
Hu, B., Gao, Y., Liu, L., and Ma, H. (2018). Federated Region-Learning: An Edge Computing Based Framework for Urban Environment Sensing. In 2018 IEEE Global Communications Conference (GLOBECOM), pages 1-7.
Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., and Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and ¡0.5MB model size.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. In Pereira, F., Burges, C. J. C., Bottou, L., and Weinberger, K. Q., editors, Advances in Neural Information Processing Systems, volume 25, pages 1097-1105. Curran Associates, Inc.
Li, Q., Wen, Z., and He, B. (2020a). Practical federated gradient boosting decision trees. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04):4642-4649.
Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., Liu, X., and He, B. (2021). A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection. IEEE Transactions on Knowledge and Data Engineering, pages 1-1.
Li, T., Sahu, A. K., Talwalkar, A., and Smith, V. (2020b). Federated Learning: Challenges, Methods, and Future Directions. IEEE Signal Processing Magazine, 37(3):50-60.
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V. (2020c). Federated Optimization in Heterogeneous Networks. In Dhillon, I., Papailiopoulos, D., and Sze, V., editors, Proceedings of Machine Learning and Systems, volume 2, pages 429-450.
Lin, Z., Mu, S., Shi, A., Pang, C., Sun, X., et al. (2018). A novel method of maize leaf disease image identification based on a multichannel convolutional neural network. Transactions of the ASABE, 61(5):1461-1474.
Liu, J., Huang, J., Zhou, Y., Li, X., Ji, S., Xiong, H., and Dou, D. (2022). From distributed machine learning to federated learning: a survey. Knowledge and Information Systems, 64(4):885-917.
Ma, N., Zhang, X., Zheng, H.-T., and Sun, J. (2018). ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. In Proceedings of the European Conference on Computer Vision (ECCV), pages 116-131.
McMahan, B., Moore, E., Ramage, D., Hampson, S., and Arcas, B. A. y. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data. In Singh, A. and Zhu, J., editors, Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, volume 54 of Proceedings of Machine Learning Research, pages 1273-1282. PMLR.
Mohanty, S. P., Hughes, D. P., and Salathé, M. (2016). Using Deep Learning for Image-Based Plant Disease Detection. Frontiers in Plant Science, 7.
Moreira, R., Rodrigues Moreira, L. F., Munhoz, P. L. A., Lopes, E. A., and Ruas, R. A. A. (2022). AgroLens: A low-cost and green-friendly Smart Farm Architecture to support real-time leaf disease diagnostics. Internet of Things, 19:100570.
Mowla, N. I., Tran, N. H., Doh, I., and Chae, K. (2020). Federated learning-based cognitive detection of jamming attack in flying ad-hoc network. IEEE Access, 8:4338-4350.
Mugunthan, V., Peraire-Bueno, A., and Kagal, L. (2020). PrivacyFL: A Simulator for Privacy-Preserving and Secure Federated Learning. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, CIKM '20, page 3085-3092, New York, NY, USA. Association for Computing Machinery.
Naeem, M., Jamal, T., Diaz-Martinez, J., Butt, S. A., Montesano, N., Tariq, M. I., De-la Hoz-Franco, E., and De-La-Hoz-Valdiris, E. (2022). Trends and Future Perspective Challenges in Big Data. In Pan, J.-S., Balas, V. E., and Chen, C.-M., editors, Advances in Intelligent Data Analysis and Applications, pages 309-325, Singapore. Springer Singapore.
Panigrahi, K. P., Das, H., Sahoo, A. K., and Moharana, S. C. (2020). Maize Leaf Disease Detection and Classification Using Machine Learning Algorithms. In Das, H., Pattnaik, P. K., Rautaray, S. S., and Li, K.-C., editors, Progress in Computing, Analytics and Networking, pages 659-669, Singapore. Springer Singapore.
Roese, A. D., Zielinski, E. C., and May De Mio, L. L. (2020). Plant diseases in afforested crop-livestock systems in Brazil. Agricultural Systems, 185:102935.
Sharma, A., Jain, A., Gupta, P., and Chowdary, V. (2021). Machine Learning Applications for Precision Agriculture: A Comprehensive Review. IEEE Access, 9:4843-4873.
Simonyan, K. and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR, abs/1409.1556.
Subramanian, M., L.V., N. P., B., J., A., M. B., and VE, S. (2022a). Hyperparameter Optimization for Transfer Learning of VGG16 for Disease Identification in Corn Leaves Using Bayesian Optimization. Big Data, 10(3):215-229. PMID: 34851735.
Subramanian, M., Shanmugavadivel, K., and Nandhini, P. S. (2022b). On fine-tuning deep learning models using transfer learning and hyper-parameters optimization for disease identification in maize leaves. Neural Computing and Applications.
Tan, P.-N., Steinbach, M., Karpatne, A., and Kumar, V. (2018). Introduction to Data Mining (2nd Edition). Pearson, 2nd edition.
Truex, S., Baracaldo, N., Anwar, A., Steinke, T., Ludwig, H., Zhang, R., and Zhou, Y. (2019). A Hybrid Approach to Privacy-Preserving Federated Learning. In Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security, AISec'19, page 1-11, New York, NY, USA. Association for Computing Machinery.
Waheed, A., Goyal, M., Gupta, D., Khanna, A., Hassanien, A. E., and Pandey, H. M. (2020). An optimized dense convolutional neural network model for disease recognition and classification in corn leaf. Computers and Electronics in Agriculture, 175:105456.
Ye, Y., Li, S., Liu, F., Tang, Y., and Hu, W. (2020). EdgeFed: Optimized Federated Learning Based on Edge Computing. IEEE Access, 8:209191-209198.
Zeng, D., Liang, S., Hu, X., and Xu, Z. (2021). FedLab: A Flexible Federated Learning Framework. arXiv preprint arXiv:2107.11621.
Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., and Gao, Y. (2021). A survey on federated learning. Knowledge-Based Systems, 216:106775.
Zhang, X., Qiao, Y., Meng, F., Fan, C., and Zhang, M. (2018). Identification of Maize Leaf Diseases Using Improved Deep Convolutional Neural Networks. IEEE Access, 6:30370-30377.
Zhong, Z., Zhou, Y., Wu, D., Chen, X., Chen, M., Li, C., and Sheng, Q. Z. (2021). P-fedavg: Parallelizing federated learning with theoretical guarantees. In IEEE INFOCOM 2021 IEEE Conference on Computer Communications, pages 1-10.