Maize leaf disease classification using convolutional neural networks and hyperparameter optimization
Resumo
Maize is an important food crop in the world, but several diseases affect the quality and quantity of agricultural production. Identifying these diseases is a very subjective and time-consuming task. The use of computer vision techniques allows automatizing this task and is essential in agricultural applications. In this study, we assess the performance of three state-of-the-art convolutional neural network architectures to classify maize leaf diseases. We apply enhancement methods such as Bayesian hyperparameter optimization, data augmentation, and fine-tuning strategies. We evaluate these CNNs on the maize leaf images from PlantVillage dataset, and all experiments were validated using a five-fold cross-validation procedure over the training and test sets. Our findings include the correlation between the maize leaf classes and the impact of data augmentation in pre-trained models. The results show that maize leaf disease classification reached 97% of accuracy for all CNNs models evaluated. Also, our approach provides new perspectives for the identification of leaf diseases based on computer vision strategies.
Palavras-chave:
Convolutional neural networks, maize leaf, classification, data augmentation, hyperparameter, Bayesian optimization
Referências
Nikos Alexandratos and Jelle Bruinsma. World agriculture towards 2030/2050: the 2012 revision. Technical report, Jun 2012. FAO Document Repository: <http://www.fao.org/3/aap106e.pdf>.
World Health Organization et al. The state of food security and nutrition in the world 2018: building climate resilience for food security and nutrition. Food & Agriculture Org., 2018.
Bekele Shiferaw, Boddupalli M. Prasanna, Jonathan Hellin, and Marianne Bänziger. Crops that feed the world 6. past successes and future challenges to the role played by maize in global food security. Food Security, 3(3):307, Aug 2011.
Jayme Garcia Arnal Barbedo. A review on the main challenges in automatic plant disease identification based on visible range images. Biosystems Engineering, 144:52 – 60, 2016.
Sharada P. Mohanty, David P. Hughes, and Marcel Salathé. Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7(September):1–10, 2016.
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org.
M. A. Ponti, L. S. F. Ribeiro, T. S. Nazare, T. Bui, and J. Collomosse. Everything you wanted to know about deep learning for computer vision but were afraid to ask. In 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T), pages 17–41, Oct 2017.
Srdjan Sladojevic, Marko Arsenovic, Andras Anderla, Dubravko Culibrk, and Darko Stefanovic. Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification. Computational Intelligence and Neuroscience, 2016, 2016.
Edna Chebet Too, Li Yujian, Sam Njuki, and Liu Yingchun. A comparative study of fine-tuning deep learning models for plant disease identification. Computers and Electronics in Agriculture, 161:272 – 279, 2019. BigData and DSS in Agriculture.
Chad DeChant, Tyr Wiesner-Hanks, Siyuan Chen, Ethan L. Stewart, Jason Yosinski, Michael A. Gore, Rebecca J. Nelson, and Hod Lipson. Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning. Phytopathology®, 107(11):1426–1432, 2017. PMID: 28653579.
X. Zhang, Y. Qiao, F. Meng, C. Fan, and M. Zhang. Identification of maize leaf diseases using improved deep convolutional neural networks. IEEE Access, 6:30370–30377, 2018.
Zhongqi Lin, Shaomin Mu, Aiju Shi, Chao Pang, Xiaoxiao Sun, et al. A novel method of maize leaf disease image identification based on a multichannel convolutional neural network. Transactions of the ASABE, 61(5):1461–1474, 2018.
Enquhone Alehegn. Ethiopian maize diseases recognition and classification using support vector machine. International Journal of Computational Vision and Robotics, 9(1):90–109, 2019.
Prakruti Bhatt, Sanat Sarangi, Anshul Shivhare, Dineshkumar Singh, and Srinivasu Pappula. Identification of diseases in corn leaves using convolutional neural networks and boosting. In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,, pages 894–899. INSTICC, SciTePress, 2019.
Ramar Ahila Priyadharshini, Selvaraj Arivazhagan, Madakannu Arun, and Annamalai Mirnalini. Maize leaf disease classification using deep convolutional neural networks. Neural Computing and Applications, 31(12):8887–8895, Dec 2019.
Malusi Sibiya and Mbuyu Sumbwanyambe. A computational procedure for the recognition and classification of maize leaf diseases out of healthy leaves using convolutional neural networks. AgriEngineering, 1(1):119–131, 2019.
Rongjie Hu, Shan Zhang, Peng Wang, Guoming Xu, Daoyong Wang, and Yuqi Qian. The identification of corn leaf diseases based on transfer learning and data augmentation. In Proceedings of the 2020 3rd International Conference on Computer Science and Software Engineering, CSSE 2020, page 58–65, New York, NY, USA, 2020. Association for Computing Machinery.
Abdul Waheed, Muskan Goyal, Deepak Gupta, Ashish Khanna, Aboul Ella Hassanien, and Hari Mohan Pandey. An optimized dense convolutional neural network model for disease recognition and classification in corn leaf. Computers and Electronics in Agriculture, 175:105456, 2020.
Ian Dewancker, Michael McCourt, Scott Clark, Patrick Hayes, Alexandra Johnson, and George Ke. A stratified analysis of bayesian optimization methods, 2016.
James S. Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl. Algorithms for hyper-parameter optimization. In J. Shawe-Taylor, R. S. Zemel, P. L. Bartlett, F. Pereira, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 24, pages 2546– 2554. Curran Associates, Inc., 2011.
Jasper Snoek, Hugo Larochelle, and Ryan P Adams. Practical bayesian optimization of machine learning algorithms. In F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 25, pages 2951–2959. Curran Associates, Inc., 2012.
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105, 2012.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
Forrest N. Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, William J. Dally, and Kurt Keutzer. Squeezenet: Alexnet-level accuracy with 50x fewer parameters and <0.5mb model size, 2016.
Yoshua Bengio. Practical recommendations for gradient-based training of deep architectures. In Neural Networks: Tricks of the Trade: Second Edition, pages 437–478, Berlin, Heidelberg, 2012. Springer Berlin Heidelberg.
Alex Krizhevsky. One weird trick for parallelizing convolutional neural networks. CoRR, abs/1404.5997, 2014.
Martin Pelikan, David E. Goldberg, and Erick Cantú-Paz. Boa: The bayesian optimization algorithm. In Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation - Volume 1, GECCO’99, page 525–532, San Francisco, CA, USA, 1999. Morgan Kaufmann Publishers Inc.
Carl Edward Rasmussen and Christopher K. I. Williams. Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). The MIT Press, 2005.
Pierre A. Devijver and Josef Kittler. Pattern Recognition: A Statistical Approach. Prentice-Hall, 1982.
Richard O. Duda, Peter E. Hart, and David G. Stork. Pattern Classification (2Nd Edition). Wiley-Interscience, New York, NY, USA, 2000.
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32, pages 8026–8037. Curran Associates, Inc., 2019.
J. Deng, W. Dong, R. Socher, L. J. Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pages 248–255, June 2009.
World Health Organization et al. The state of food security and nutrition in the world 2018: building climate resilience for food security and nutrition. Food & Agriculture Org., 2018.
Bekele Shiferaw, Boddupalli M. Prasanna, Jonathan Hellin, and Marianne Bänziger. Crops that feed the world 6. past successes and future challenges to the role played by maize in global food security. Food Security, 3(3):307, Aug 2011.
Jayme Garcia Arnal Barbedo. A review on the main challenges in automatic plant disease identification based on visible range images. Biosystems Engineering, 144:52 – 60, 2016.
Sharada P. Mohanty, David P. Hughes, and Marcel Salathé. Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7(September):1–10, 2016.
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org.
M. A. Ponti, L. S. F. Ribeiro, T. S. Nazare, T. Bui, and J. Collomosse. Everything you wanted to know about deep learning for computer vision but were afraid to ask. In 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T), pages 17–41, Oct 2017.
Srdjan Sladojevic, Marko Arsenovic, Andras Anderla, Dubravko Culibrk, and Darko Stefanovic. Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification. Computational Intelligence and Neuroscience, 2016, 2016.
Edna Chebet Too, Li Yujian, Sam Njuki, and Liu Yingchun. A comparative study of fine-tuning deep learning models for plant disease identification. Computers and Electronics in Agriculture, 161:272 – 279, 2019. BigData and DSS in Agriculture.
Chad DeChant, Tyr Wiesner-Hanks, Siyuan Chen, Ethan L. Stewart, Jason Yosinski, Michael A. Gore, Rebecca J. Nelson, and Hod Lipson. Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning. Phytopathology®, 107(11):1426–1432, 2017. PMID: 28653579.
X. Zhang, Y. Qiao, F. Meng, C. Fan, and M. Zhang. Identification of maize leaf diseases using improved deep convolutional neural networks. IEEE Access, 6:30370–30377, 2018.
Zhongqi Lin, Shaomin Mu, Aiju Shi, Chao Pang, Xiaoxiao Sun, et al. A novel method of maize leaf disease image identification based on a multichannel convolutional neural network. Transactions of the ASABE, 61(5):1461–1474, 2018.
Enquhone Alehegn. Ethiopian maize diseases recognition and classification using support vector machine. International Journal of Computational Vision and Robotics, 9(1):90–109, 2019.
Prakruti Bhatt, Sanat Sarangi, Anshul Shivhare, Dineshkumar Singh, and Srinivasu Pappula. Identification of diseases in corn leaves using convolutional neural networks and boosting. In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,, pages 894–899. INSTICC, SciTePress, 2019.
Ramar Ahila Priyadharshini, Selvaraj Arivazhagan, Madakannu Arun, and Annamalai Mirnalini. Maize leaf disease classification using deep convolutional neural networks. Neural Computing and Applications, 31(12):8887–8895, Dec 2019.
Malusi Sibiya and Mbuyu Sumbwanyambe. A computational procedure for the recognition and classification of maize leaf diseases out of healthy leaves using convolutional neural networks. AgriEngineering, 1(1):119–131, 2019.
Rongjie Hu, Shan Zhang, Peng Wang, Guoming Xu, Daoyong Wang, and Yuqi Qian. The identification of corn leaf diseases based on transfer learning and data augmentation. In Proceedings of the 2020 3rd International Conference on Computer Science and Software Engineering, CSSE 2020, page 58–65, New York, NY, USA, 2020. Association for Computing Machinery.
Abdul Waheed, Muskan Goyal, Deepak Gupta, Ashish Khanna, Aboul Ella Hassanien, and Hari Mohan Pandey. An optimized dense convolutional neural network model for disease recognition and classification in corn leaf. Computers and Electronics in Agriculture, 175:105456, 2020.
Ian Dewancker, Michael McCourt, Scott Clark, Patrick Hayes, Alexandra Johnson, and George Ke. A stratified analysis of bayesian optimization methods, 2016.
James S. Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl. Algorithms for hyper-parameter optimization. In J. Shawe-Taylor, R. S. Zemel, P. L. Bartlett, F. Pereira, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 24, pages 2546– 2554. Curran Associates, Inc., 2011.
Jasper Snoek, Hugo Larochelle, and Ryan P Adams. Practical bayesian optimization of machine learning algorithms. In F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 25, pages 2951–2959. Curran Associates, Inc., 2012.
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105, 2012.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
Forrest N. Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, William J. Dally, and Kurt Keutzer. Squeezenet: Alexnet-level accuracy with 50x fewer parameters and <0.5mb model size, 2016.
Yoshua Bengio. Practical recommendations for gradient-based training of deep architectures. In Neural Networks: Tricks of the Trade: Second Edition, pages 437–478, Berlin, Heidelberg, 2012. Springer Berlin Heidelberg.
Alex Krizhevsky. One weird trick for parallelizing convolutional neural networks. CoRR, abs/1404.5997, 2014.
Martin Pelikan, David E. Goldberg, and Erick Cantú-Paz. Boa: The bayesian optimization algorithm. In Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation - Volume 1, GECCO’99, page 525–532, San Francisco, CA, USA, 1999. Morgan Kaufmann Publishers Inc.
Carl Edward Rasmussen and Christopher K. I. Williams. Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). The MIT Press, 2005.
Pierre A. Devijver and Josef Kittler. Pattern Recognition: A Statistical Approach. Prentice-Hall, 1982.
Richard O. Duda, Peter E. Hart, and David G. Stork. Pattern Classification (2Nd Edition). Wiley-Interscience, New York, NY, USA, 2000.
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32, pages 8026–8037. Curran Associates, Inc., 2019.
J. Deng, W. Dong, R. Socher, L. J. Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pages 248–255, June 2009.
Publicado
07/10/2020
Como Citar
DA ROCHA, Erik Lucas; RODRIGUES, Larissa; MARI, João Fernando.
Maize leaf disease classification using convolutional neural networks and hyperparameter optimization. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 16. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 104-110.
DOI: https://doi.org/10.5753/wvc.2020.13489.
