A lightweight plant disease recognition network based on ResNet

Research Article
Open access

A lightweight plant disease recognition network based on ResNet

Yunlong Duan 1 , Ziyu Han 2 , Zhening Tang 3*
  • 1 Guangzhou University Sontan College, Guangzhou, 510275, China    
  • 2 Jiangxi University of Finance and Economics, Nanchang, 330013, China    
  • 3 Shanghai University of Finance and Economics, Shanghai, 200433, China    
  • *corresponding author tangzhening@163.sufe.edu.cn
ACE Vol.5
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-57-7
ISBN (Online): 978-1-915371-58-4

Abstract

Identification of foliar diseases is very important for the cultivation of plants. If no diseases are found, the cultivation results may decline, resulting in serious losses of related industries. Most of the early automatic recognition methods of plant leaves are based on manual features and classifiers, and the recognition performance is often unable to meet the actual complex application scenarios. Thanks to the rapid development of convolutional neural networks, such as ResNet, the accuracy of plant disease identification based on deep learning has made a breakthrough. However, convolutive neural network tends to have too many parameters, large amount of calculation and slow training speed, which is difficult to be used in various small and medium-sized plant cultivation industries, especially in small edge computing devices deployed in the field. This paper designs a new lightweight Resnet network structure, namely Resnet-9. The number of network layers in traditional Resnet is reduced. Compared with other commonly used plant disease recognition methods, the accuracy of Resnet is guaranteed and the network is more lightweight. The parameter of this model occupies only 6.6M memory and achieves 99.23% accuracy on public datasets. Even in the other data sets, the accuracy was still 95.15%. The effectiveness of the method is verified by comparative experiment.

Keywords:

plant leaf diseases, deep learning, Resnet, lightweight

Duan,Y.;Han,Z.;Tang,Z. (2023). A lightweight plant disease recognition network based on ResNet. Applied and Computational Engineering,5,583-592.
Export citation

References

[1]. Q. Huang and Q. Dong, "Image Classification of Crop Diseases Based on Convolutional Neural Network" China Computer & Communication, vol. 34, pp. 138–142+146, 2022.

[2]. K. He, X. Zhang, S. Ren, et al., "Deep Residual Learning for Image Recognition," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, 2016.

[3]. B. Godi, A. S. Muttipati, M. P. Rao, et al., "ResNet Model to Forecast Plant Leaf Disease," In 2022 International Conference on Computing, Communication and Power Technology (IC3P), 2022, pp. 38–43.

[4]. V. Kumar, H. Arora and J. Sisodia, "ResNet-based approach for Detection and Classification of Plant Leaf Diseases," In 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), 2020, pp. 495–502.

[5]. X. Li and L. Rai, "Apple Leaf Disease Identification and Classification using ResNet Models," In 2020 IEEE 3rd International Conference on Electronic Information and Communication Technology (ICEICT), 2020, pp. 738–742.

[6]. D. He, P. Wang, T. Niu, et al., "Classification Model of Grape Downy Mildew Disease Degree in Field Based on Improved Residual Network," Journal of Agricultural Machinery, vol. 53, pp. 235–243, 2022.

[7]. Q. Li, N. Miao, X. Zhang, et al., "Image recognition of maize disease based on asymmetric convolutional attention residual network and transfer learning," Science Technology and Engineering, vol. 21, pp. 6249–6256, 2021.

[8]. J. Tie, J. Luo, L. Zheng, et al., "Citrus disease recognition based on improved residual network," Journal of South-Central University for Nationalities (Natural Science Edition), vol. 40, pp. 621–630, 2021．

[9]. L. Huang, Y. Luo, X. Yang, et al., "Crop Disease Recognition Based on Attention Mechanism and Multi-scale Residual Network," Journal of Agricultural Machinery, vol.52, pp. 264–271, 2021.

[10]. J. Chen, D. Zhang, A. Zeb, et al., "Identification of rice plant diseases using lightweight attention networks," Expert Systems with Applications, vol. 169, pp. 114514, 2021.

[11]. A. Veit, M.J. Wilber, S. Belongie, "Residual Networks Behave Like Ensembles of Relatively Shallow Networks," Advances in Neural Information Processing Systems, vol. 29, 2016.

[12]. S. Ioffe and C. Szegedy, "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift," In International Conference on Machine Learning, PMLR, 2015, pp. 448–456.

[13]. A.F. Agarap, "Deep Learning using Rectified Linear Units (ReLU)," arXiv preprint arXiv:1803.08375, 2018.

[14]. Z. Zhang, M. Sabuncu, "Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels," Advances in Neural Information Processing Systems, vol. 31, 2018.

[15]. github: Dataset of diseased plant leaf images and corresponding labels. [Online]. Available: https://github.com/spMohanty/PlantVillage-Dataset

[16]. kaggle: PlantifyDr Dataset. [Online]. Available: https://www.kaggle.com/datasets/lavaman151/plantifydr-dataset

[17]. P. Molchanov, S. Tyree, T. Karras, et al., "Pruning Convolutional Neural Networks for Resource Efficient Inference," In Proceedings of the International Conference on Learning Representations (ICLR), 2017.

[18]. P. Jiang, Y. Chen, B. Liu, et al., "Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks," IEEE Access, vol. 7, pp. 59069–59080, 2019.

[19]. X. Zhang, L. Han, Y. Dong, et al., "A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images," Remote Sens, vol. 11, no. 13, pp. 1554, 2019.

[20]. J. Amara, B. Bouaziz and A. Algergawy, "A Deep Learning-based Approach for Banana Leaf Diseases Classification," In BTW (Workshops), 2017, pp. 79–88.

[21]. M. Brahimi, K. Boukhalfa and A. Moussaoui, "Deep learning for tomato diseases: Classification and symptoms visualization," Applied Artificial Intelligence, vol. 31, no. 4, pp. 299–315, 2017.

[22]. K.C. Kamal, Z. Yin, M. Wu, et al., "Depthwise separable convolution architectures for plant disease classification," Computers and Electronics in Agriculture, vol. 165, pp. 104948, 2019.

[23]. M. Denil, B. Shakibi, L. Dinh, et al., "Predicting Parameters in Deep Learning," In Conference and Workshop on Neural Information Processing Systems (NIPS), vol. 26, 2013.

[24]. M.C. Leong, D.K. Prasad, Y.T. Lee, et al., "Semi-CNN architecture for effective spatio-temporal learning in action recognition," Applied Sciences, vol. 10, no.2, pp. 557, 2020.

Cite this article

Duan,Y.;Han,Z.;Tang,Z. (2023). A lightweight plant disease recognition network based on ResNet. Applied and Computational Engineering,5,583-592.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-57-7(Print) / 978-1-915371-58-4(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.5
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. Q. Huang and Q. Dong, "Image Classification of Crop Diseases Based on Convolutional Neural Network" China Computer & Communication, vol. 34, pp. 138–142+146, 2022.

[2]. K. He, X. Zhang, S. Ren, et al., "Deep Residual Learning for Image Recognition," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, 2016.

[3]. B. Godi, A. S. Muttipati, M. P. Rao, et al., "ResNet Model to Forecast Plant Leaf Disease," In 2022 International Conference on Computing, Communication and Power Technology (IC3P), 2022, pp. 38–43.

[4]. V. Kumar, H. Arora and J. Sisodia, "ResNet-based approach for Detection and Classification of Plant Leaf Diseases," In 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), 2020, pp. 495–502.

[5]. X. Li and L. Rai, "Apple Leaf Disease Identification and Classification using ResNet Models," In 2020 IEEE 3rd International Conference on Electronic Information and Communication Technology (ICEICT), 2020, pp. 738–742.

[6]. D. He, P. Wang, T. Niu, et al., "Classification Model of Grape Downy Mildew Disease Degree in Field Based on Improved Residual Network," Journal of Agricultural Machinery, vol. 53, pp. 235–243, 2022.

[7]. Q. Li, N. Miao, X. Zhang, et al., "Image recognition of maize disease based on asymmetric convolutional attention residual network and transfer learning," Science Technology and Engineering, vol. 21, pp. 6249–6256, 2021.

[8]. J. Tie, J. Luo, L. Zheng, et al., "Citrus disease recognition based on improved residual network," Journal of South-Central University for Nationalities (Natural Science Edition), vol. 40, pp. 621–630, 2021．

[9]. L. Huang, Y. Luo, X. Yang, et al., "Crop Disease Recognition Based on Attention Mechanism and Multi-scale Residual Network," Journal of Agricultural Machinery, vol.52, pp. 264–271, 2021.

[10]. J. Chen, D. Zhang, A. Zeb, et al., "Identification of rice plant diseases using lightweight attention networks," Expert Systems with Applications, vol. 169, pp. 114514, 2021.

[11]. A. Veit, M.J. Wilber, S. Belongie, "Residual Networks Behave Like Ensembles of Relatively Shallow Networks," Advances in Neural Information Processing Systems, vol. 29, 2016.

[12]. S. Ioffe and C. Szegedy, "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift," In International Conference on Machine Learning, PMLR, 2015, pp. 448–456.

[13]. A.F. Agarap, "Deep Learning using Rectified Linear Units (ReLU)," arXiv preprint arXiv:1803.08375, 2018.

[14]. Z. Zhang, M. Sabuncu, "Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels," Advances in Neural Information Processing Systems, vol. 31, 2018.

[15]. github: Dataset of diseased plant leaf images and corresponding labels. [Online]. Available: https://github.com/spMohanty/PlantVillage-Dataset

[16]. kaggle: PlantifyDr Dataset. [Online]. Available: https://www.kaggle.com/datasets/lavaman151/plantifydr-dataset

[17]. P. Molchanov, S. Tyree, T. Karras, et al., "Pruning Convolutional Neural Networks for Resource Efficient Inference," In Proceedings of the International Conference on Learning Representations (ICLR), 2017.

[18]. P. Jiang, Y. Chen, B. Liu, et al., "Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks," IEEE Access, vol. 7, pp. 59069–59080, 2019.

[19]. X. Zhang, L. Han, Y. Dong, et al., "A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images," Remote Sens, vol. 11, no. 13, pp. 1554, 2019.

[20]. J. Amara, B. Bouaziz and A. Algergawy, "A Deep Learning-based Approach for Banana Leaf Diseases Classification," In BTW (Workshops), 2017, pp. 79–88.

[21]. M. Brahimi, K. Boukhalfa and A. Moussaoui, "Deep learning for tomato diseases: Classification and symptoms visualization," Applied Artificial Intelligence, vol. 31, no. 4, pp. 299–315, 2017.

[22]. K.C. Kamal, Z. Yin, M. Wu, et al., "Depthwise separable convolution architectures for plant disease classification," Computers and Electronics in Agriculture, vol. 165, pp. 104948, 2019.

[23]. M. Denil, B. Shakibi, L. Dinh, et al., "Predicting Parameters in Deep Learning," In Conference and Workshop on Neural Information Processing Systems (NIPS), vol. 26, 2013.

[24]. M.C. Leong, D.K. Prasad, Y.T. Lee, et al., "Semi-CNN architecture for effective spatio-temporal learning in action recognition," Applied Sciences, vol. 10, no.2, pp. 557, 2020.

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.