Plant Disease Detection with Remedial Solution

Authors

  • G. Santhoshi Assistant Professor, Dept of Information Technology, G. Narayanamma Institute of Technology and Science for Women, Hyderabad, India. Author
  • Kovvuri Ramya Sri Assistant Professor, Dept of Computer Science Technology, G. Narayanamma Institute of Technology and Science for Women, Hyderabad, India. Author
  • M. Jyothi Assistant Professor, Dept of Information Technology, G. Narayanamma Institute of Technology and Science for Women, Hyderabad, India. Author
  • N. Chandana Student, Dept of Information Technology, G. Narayanamma Institute of Technology and Science for Women, Hyderabad, India. Author
  • K. Anjali Student, Dept of Information Technology, G. Narayanamma Institute of Technology and Science for Women, Hyderabad, India. Author
  • B. Meena Student, Dept of Information Technology, G. Narayanamma Institute of Technology and Science for Women, Hyderabad, India. Author

DOI:

https://doi.org/10.47392/IRJAEM.2024.0360

Keywords:

Feature extraction, Convolutional Neural Networks (CNN)

Abstract

Agriculture sector as well as organic farming is playing vital role in 21st Century. The traditional methods of plant disease detection is cost effective as well as time consuming process.so this project aims for a web application which acts as user-friendly application for the farmers and supports the organic farming where they can capture the images of leaves and instantly they receive the name of the disease along with the description of disease and prevention methods of plant disease. To make the application user-friendly for the uneducated farmers also we have added the feature called supplements which gives the image of the fertilizer that the farmer have to use for the Plant disease. By clicking on the buy product option the farmer can also buy the fertilizer that they want according to the quantity that they need for the farms. So these web application use the Convolutional neural networks (CNN) technology, feature extraction extracts into many layers and identify the disease.

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Published

2024-08-06