Grape Disease Detection Using Image Processing

Authors

  • Prof. Yogita Pore  Computer Department, Zeal College of Engineering and Research Pune, Maharashtra, India
  • Suyog Arote  Computer Department, Zeal College of Engineering and Research Pune, Maharashtra, India
  • Siddhant Ayachit  Computer Department, Zeal College of Engineering and Research Pune, Maharashtra, India
  • Nishant Bangar  Computer Department, Zeal College of Engineering and Research Pune, Maharashtra, India
  • Sarthak Ekhande  Computer Department, Zeal College of Engineering and Research Pune, Maharashtra, India

Keywords:

grape leaf, Diseases Predication, CNN, Neural Network

Abstract

In India, grape cultivation is both social and economic. Maharashtra is the leading grape producer in India. Grape quality has decreased in recent years due to a variety of factors. Grape infections are one of the major causes. Farmers spray massive amounts of pesticides to prevent illnesses, which raises production costs. Farmers are also unable to manually diagnose illnesses. The illnesses are only discovered after they have become infected, which takes a long time and has negative consequences for the vineyard. The goal of the proposed research is to create a monitoring system that would detect grape illnesses in their early stages using a CNN algorithm and provide notifications to the farmer and expert. The goal of this project is to provide early detection of Grape Leaf Disease

References

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International Journal of Scientific Research in Science and Technology

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Published

2023-06-30

Issue

Volume 10, Issue 3, May-June-2023

Section

Research Articles

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Copyright (c) IJSRST

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Prof. Yogita Pore, Suyog Arote, Siddhant Ayachit, Nishant Bangar, Sarthak Ekhande, " Grape Disease Detection Using Image Processing, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 10, Issue 3, pp.547-552, May-June-2023.