Advertisement

A Model for Prediction of Paddy Crop Disease Using CNN

  • Ritesh SharmaEmail author
  • Sujay Das
  • Mahendra Kumar Gourisaria
  • Siddharth Swarup Rautaray
  • Manjusha Pandey
Conference paper
  • 11 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1119)

Abstract

The agriculture industry is the most important industry for society as it serves the most important need of life. But the plant diseases in agriculture lead to a decrease in productivity and hence it is very important to prevent, detect, and get rid of the diseases. Image processing and deep learning are nowadays the buzzwords in the IT industry and their applications in the agriculture industry can enhance decision making in various aspects of the agriculture industry. Paddy crop is one of the most demanding crops especially in South Asia. This paper proposes a predictive model using CNN for classification and prediction of disease in paddy crop. Paddy crop diseases are very fatal and can affect the crops severely if it is not taken care in the initial stages. The proposed model will improve the decision making using CNN in case of various diseases in paddy crop for prediction of diseases in initial stages and prevention of mass loss in productivity of the whole yield.

Keywords

Convolutional neural networks Image processing Paddy crop diseases 

References

  1. 1.
    Suraksha, I.S., Sushma, B., Sushma, R.G., Susmitha, K., Uday Shankar, S.V.: Disease prediction of paddy crops using data mining and image processing techniques. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 5(6) (2016)Google Scholar
  2. 2.
    Rajmodhan, R., Pajany, M., Rajesh, R., Raghuraman, D., Prabu, U.: Smart paddy crop disease identification using deep convolutional neural network and SVM classifier. Int. J. Pure Appl. Math. 118(15) (2018)Google Scholar
  3. 3.
    Barik, L.: A survey on region identification of rice disease using image processing. Int. J. Res. Sci. Innov. 5(1) (2018)Google Scholar
  4. 4.
    Jagan Mohan, K., Balasubramanian, M., Palanivel, S.: Detection and Recognition of Diseases from Paddy Plat Leaf, International Journal of Computer Applications Vol. 144 No. 12(2016)Google Scholar
  5. 5.
    Badage, A.: Crop disease detection using machine learning: indian agriculture. Int. Res. J. Eng. Technol. 5(9) (2018)Google Scholar
  6. 6.
    Dhaygude, S.B., Kumbhar, N.P.: Agricultural plant leaf disease detection using image processing. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 2(1) (2013)Google Scholar
  7. 7.
  8. 8.
  9. 9.
  10. 10.
  11. 11.
    Rice knowledge management portal. http://www.rkmp.co.in/content/leaf-smut-2
  12. 12.
    Jaswal, D., Sowmya, V., Soman, K.P.: Image classification using convolutional neural networks. Int. J. Adv. Res. Technol. 3(6) (2014)Google Scholar
  13. 13.
    Scherer, D., Muller, A., Behnke, S.: Evaluation of pooling operations in convolutional architectures for object recognition. In: International Conference on Artificial Neural Networks (ICANN) (2010)Google Scholar
  14. 14.
    Ullah, I.: Data mining algorithms and medical sciences. Int. J. Comput. Sci. Inf. Technol. (IJCSIT) 2(6) (2010)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Ritesh Sharma
    • 1
    Email author
  • Sujay Das
    • 2
  • Mahendra Kumar Gourisaria
    • 1
  • Siddharth Swarup Rautaray
    • 1
  • Manjusha Pandey
    • 1
  1. 1.School of Computer EngineeringKIIT Deemed UniversityBhubaneshwarIndia
  2. 2.School of Electronics EngineeringKIIT Deemed UniversityBhubaneshwarIndia

Personalised recommendations