Advertisement

Impact of Hyperparameter Tuning on Deep Learning Based Estimation of Disease Severity in Grape Plant

  • Shradha VermaEmail author
  • Anuradha Chug
  • Amit Prakash Singh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 978)

Abstract

Accurate and quantitative estimation of disease severity in plants is a complex task, even for experienced agronomists and plant pathologists, where incorrect evaluation might lead to the inappropriate use of pesticides. In this paper, the authors have utilized two Convolutional Neural Networks (CNN), namely, AlexNet and ResNet18, for assessing the disease severity in Grape plant. The images for Isariopsis Leaf Spot disease in Grape plant, also known as Leaf Blight, were taken from the PlantVillage dataset, divided into three categories of severity stages (early, middle & end) and used for training the CNN models, via the transfer learning approach. The effects of fine-tuning the hyperparameters such as mini-batch size, epochs and data augmentation were also observed and analysed. For performance comparison, measures such as classification accuracy, mean F1-score, mean precision, mean recall, validation loss, ROC curves and time taken were recorded.

Keywords

Deep learning Convolutional neural networks (CNN) Hyperparameter tuning Plant diseases Disease severity Grape plant 

Notes

Acknowledgments

This study was supported by Department of Science and Technology (DST), Government of India, New Delhi, under Interdisciplinary Cyber Physical Systems (ICPS) Programme (Reference No. T-319).

References

  1. 1.
    Giomi T, Runhaar P, Runhaar H (2018) Reducing agrochemical use for nature conservation by Italian olive farmers: An evaluation of public and private governance strategies. International journal of agricultural sustainability. 16(1):94–105CrossRefGoogle Scholar
  2. 2.
    Pal T, Jaiswal V, Chauhan RS (2016) DRPPP: A machine learning based tool for prediction of disease resistance proteins in plants. Comput Biol Med 78:42–48CrossRefGoogle Scholar
  3. 3.
    Camargo A, Smith JS (2009) Image pattern classification for the identification of disease causing agents in plants. Comput Electron Agric 66(2):121–125CrossRefGoogle Scholar
  4. 4.
    Al Bashish D, Braik M, Bani-Ahmad S (2011) Detection and classification of leaf diseases using K-means-based segmentation and Neural Networks-based Classification. Inf Technol J 10(2):267–275CrossRefGoogle Scholar
  5. 5.
    Camargo A, Smith JS (2009) An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosys Eng 102(1):9–21CrossRefGoogle Scholar
  6. 6.
    Kamilaris A, Prenafeta-Boldú FX (2018) Deep learning in agriculture: A survey. Comput Electron Agric 147(February):70–90CrossRefGoogle Scholar
  7. 7.
    Mohanty SP, Hughes DP, Salathé M (2016) Using Deep Learning for Image-Based Plant Disease Detection. Frontiers in Plant Science. 7(September):1–10Google Scholar
  8. 8.
    Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311–318CrossRefGoogle Scholar
  9. 9.
    Wang, G., Sun, Y., & Wang, J.: Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning. Computational Intelligence and Neuroscience. 1–8, (2017)Google Scholar
  10. 10.
    Liang Q, Xiang S, Hu Y, Coppola G, Zhang D, Sun W (2019) PD2SE-Net: Computer-assisted plant disease diagnosis and severity estimation network. Comput Electron Agric 157:518–529CrossRefGoogle Scholar
  11. 11.
    Sladojevic, S., Arsenovic, M., Stefanovic, D., Anderla, A., Culibrk, D.: Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification. Computational Intelligence and Neuroscience. 1–11, (2016)CrossRefGoogle Scholar
  12. 12.
    Verma, S., Chug, A., Singh, A. P., Sharma, S., Rajvanshi, P.: Deep Learning-Based Mobile Application for Plant Disease Diagnosis: A Proof of Concept With a Case Study on Tomato Plant. In Applications of Image Processing and Soft Computing Systems in Agriculture. 242–271, IGI Global, (2019)Google Scholar
  13. 13.
    Lu Y, Yi S, Zeng N, Liu Y, Zhang Y (2017) Identification of rice diseases using deep convolutional neural networks. Neurocomputing. 267:378–384CrossRefGoogle Scholar
  14. 14.
    Sun Y, Liu Y, Wang G, Zhang H (2017) Deep learning for plant identification in natural environment. Comput Intell Neurosci 2017:1–6Google Scholar
  15. 15.
    Zhang S, Huang W, Zhang C (2019) Three-channel convolutional neural networks for vegetable leaf disease recognition. Cogn Syst Res 53:31–41CrossRefGoogle Scholar
  16. 16.
    Toda Y, Okura F (2019) How Convolutional Neural Networks Diagnose Plant Disease. Plant Phenomics. 2019:9237136CrossRefGoogle Scholar
  17. 17.
    Krizhevsky, A., Sutskever, I., Hinton, G. E.: Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097–1105, (2012)Google Scholar
  18. 18.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770–778, (2016)Google Scholar
  19. 19.
    Hughes, D. P., Salathe, M.: An open access repository of images on plant health to enable the development of mobile disease diagnostics. ArXiv Preprint.  https://doi.org/10.1111/1755-0998.12237, (2015)
  20. 20.
    Campbell, C. L., Neher, D. A.: Estimating disease severity and incidence. In Epidemiology and management of root diseases, pp 117–147. Springer, Berlin, Heidelberg, (1994)CrossRefGoogle Scholar
  21. 21.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Shradha Verma
    • 1
    Email author
  • Anuradha Chug
    • 1
  • Amit Prakash Singh
    • 1
  1. 1.University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University (GGSIP)DelhiIndia

Personalised recommendations