Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32138
Comparison of Deep Convolutional Neural Networks Models for Plant Disease Identification

Authors: Megha Gupta, Nupur Prakash

Abstract:

Identification of plant diseases has been performed using machine learning and deep learning models on the datasets containing images of healthy and diseased plant leaves. The current study carries out an evaluation of some of the deep learning models based on convolutional neural network architectures for identification of plant diseases. For this purpose, the publicly available New Plant Diseases Dataset, an augmented version of PlantVillage dataset, available on Kaggle platform, containing 87,900 images has been used. The dataset contained images of 26 diseases of 14 different plants and images of 12 healthy plants. The CNN models selected for the study presented in this paper are AlexNet, ZFNet, VGGNet (four models), GoogLeNet, and ResNet (three models). The selected models are trained using PyTorch, an open-source machine learning library, on Google Colaboratory. A comparative study has been carried out to analyze the high degree of accuracy achieved using these models. The highest test accuracy and F1-score of 99.59% and 0.996, respectively, were achieved by using GoogLeNet with Mini-batch momentum based gradient descent learning algorithm.

Keywords: comparative analysis, convolutional neural networks, deep learning, plant disease identification

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 386

References:


[1] “Plant Health and Food Security”, Food and Agriculture Organization of the United Nation, International Plant Protection Convention, 2017.
[2] H. Rahman et al.., “A comparative analysis of machine learning approaches for plant disease identification,” in Advancements in Life Sciences – International Quarterly Journal of Biological Sciences, pp. 120-126, Aug 2017.
[3] H. B. Prajapati, J. P. Shah and V. K. Dabhi, “Detection and Classification of Rice Plant Diseases,” in Intelligent Decision Technologies, IOS Press, pp. 357-375, 29 Aug 2017.
[4] P. Alagumariappan et al.., “Intelligent Plant Disease Identification System Using Machine Learning,” in Eng. Proc., 14 Nov 2020.
[5] A. Khan, A. Sohail, U. Zahoora and A. S. Qureshi, “A Survey of the Recent Architectures of Deep Convolutional Neural Networks,” in Artificial Intelligence Review, 21 Apr 2020.
[6] S. P. Mohanty, D. Hughes and M. Salathé, “Using Deep Learning for Image-Based Plant Disease Detection”, Frontiers in Plant Science, vol. 7, 2016.
[7] K. P. Ferentinos, “Deep learning models for plant disease detection and diagnosis,” in Computers and Electronics in Agriculture, pp. 311-318, 2018.
[8] J. Boulent, S. Foucher, J. Théau and P. St-Charles, “Convolutional Neural Networks for the Automatic Identification of Plant Diseases,” in Front. Plant Sci., 23 July 2019.
[9] J. Chen, J. Chen, D. Zhang, Y. Sun and Y. A. Nanehkaran, “Using deep transfer learning for image-based plant disease identification,” in Computers and Electronics in Agriculture, 2020.
[10] S. Bhattarai, “New Plant Diseases Dataset,” Kaggle.com, 2018. (Online). Available: https://www.kaggle.com/vipoooool/new-plant-diseases-dataset. (Accessed: 21- Apr- 2021).
[11] D. P. Hughes and M. Salathé, “An open access repository of images on plant health to enable the development of mobile disease diagnostics,” arXiv:1511.08060, 2015.
[12] A. Krizhevsky, I. Sutskever and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in NIPS, 2012.
[13] M. D. Zeiler and R. Fergus, “Visualizing and Understanding Convolutional Networks,” arXiv:1311.2901v3 (cs.CV), 28 Nov 2013.
[14] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv:1409.21556v6 (cs.CV), 10 Apr 2015.
[15] C. Szegedy et al.., “Going deeper with convolutions,” arXiv:1409.4842v1 (cs.CV), 17 Sep 2014.
[16] K. He, X. Zhang, S. Ren and J. Sun, “Deep Residual Learning for Image Recognition,” arXiv:1512.03385v1 (cs.CV), 10 Dec 2015.
[17] “Google Colaboratory”, Colab.research.google.com. (Online). Available: https://colab.research.google.com/notebooks/intro.ipynb?utm_source=scs-index#recent=true. (Accessed: 01- May- 2021).
[18] “PyTorch documentation – PyTorch 1.8.1 documentation,” PyTorch.org. (Online). Available: https://pytorch.org/docs/stable/index.html. (Accessed: 03- May- 2021).
[19] “Quickstart – Mlflow 1.17.0 documentation,” Mlflow.org. (Online). Available: https://www.mlflow.org/docs/latest/quickstart.html. (Accessed: 08- May- 2021).