Plant Disease Prediction System using Image Processing

Authors

  • B Uma Jagadeswari  Computer Science and Engineering, GVP College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India
  • D Harshitha  Computer Science and Engineering, GVP College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India
  • G Vineela  Computer Science and Engineering, GVP College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India
  • B Siri  Computer Science and Engineering, GVP College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India
  • Yaragani Sowmya  Assistant Professsor, Computer Science and Engineering, GVP College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India

DOI:

https://doi.org//10.32628/CSEIT1206333

Keywords:

Image Processing, CNN, Plant Disease Identification.

Abstract

Agriculture productivity is the major issue which affects the Indian economy. Crop cultivation plays an essential role in the agricultural field. Presently, the loss of food is mainly due to infected crops, which reflexively reduces the production rate. The major cause for decrease in the quality and amount of agricultural productivity is due to the diseases in plants. The occurrence of diseases in plants may result in significant loss in both quality and quantity of agricultural productivity. This can produce the negative impact on the countries whose economies are primarily dependent on the agriculture. Farmers encounter great difficulties in detecting and controlling plant diseases. Hence the detection of plant diseases in the earlier stages is very important to avoid the loss in terms of quality, quantity and finance. This paper mainly focuses on the approach based on image processing techniques that help farmers for detecting the diseases of plants by uploading leaf image to the system.

References

  1. Plant Health Monitoring System Through Image Processing and Defects Overcoming Through Embedded System, K.Anitha, G.Keerthiga, A. Hema Malini, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8, Issue-1, May 2019
  2. Raghottam Ramesh. Kulkarni, Dr. A. V. Sutagundar “Plant Leaf Disease Management System” Proceedings of the IEEE 2017 International Conference on Computing Methodologies and Communication(ICCMC)
  3. Plant Health Monitoring Using Image Processing, Vignesh Dhandapani, S. Remya, T. Shanthi, R. Vidhy, Department of Computer Science Engineering,VSB Engineering College, Karur, Tamil Nadu, INDIA, International Journal of Engineering Research in Computer Science and Engineering (IJERCSE) Vol 5, Issue 3, March 2018
  4. Plant Disease Prediction using Image Processing Techniques- A Review, Sujeet Varshney et al, International Journal of Computer Science and Mobile Computing, Vol.5 Issue.5, May- 2016, pg. 394-398
  5. S.Raj Kumar , S.Sowrirajan,” Automatic Leaf Disease Detection and Classification using Hybrid Features and Supervised Classifier”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 5, Issue 6,2016.
  6. Plant Disease Detection using CNN & Remedy, Adnan Mushtaq Ali Karol , Drushti Gulhane , Tejal Chandiwade, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 8, Issue 3, March 2019
  7. S. Sankaran, A. Mishra, R. Ehsani, and C. Davis, “A review of advanced techniques for detecting plant diseases,” Computers and Electronics in Agriculture, vol. 72, no. 1, pp. 1–13, 2010. View at Publisher ·View at Google Scholar · View at Scopus.
  8. Atabay H. A. (2017). Deep residual learning for tomato plant leaf disease identification. J. Theor. Appl. Inform. Technol. 95, 6800–6808. 

Downloads

Published

2020-06-30

Issue

Section

Research Articles

How to Cite

[1]
B Uma Jagadeswari, D Harshitha, G Vineela, B Siri, Yaragani Sowmya, " Plant Disease Prediction System using Image Processing, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 3, pp.162-167, May-June-2020. Available at doi : https://doi.org/10.32628/CSEIT1206333