Grape Leaf Disease Detection Using Nural Network

Grape Leaf Disease Detection Using Nural Network

  • September 28 2023
  • Bhimsen

Abstract of Grape Leaf Disease Detection Project

Having diseases is quite natural in crops due to changing climatic and environmental conditions. Diseases affect the growth and produce of the crops and often difficult to control.

To ensure good quality and high production, it is necessary to have accurate disease diagnosis and control actions to prevent them in time. Grape which is widely grown crop in India and it may be affected by different types of diseases on leaf, stem and fruit.

Fruit diseases which are the early symptoms caused due to Gray mold , Healthy Grapes, powdery mildew and Sour rot. So, there is a need to have an automatic system that can be used to detect the type of diseases and to take appropriate actions suggest remedies.


Introduction to Grape Leaf Disease Identification

Indian Economy is highly dependent on agricultural productivity of the country. Grape is very commercial fruit of India. It can easily be grown in all tropical, sub-tropical and temperate climatic regions.

India has got different types of climates and soil in different parts of the country. This makes grapevines a major vegetative propagated crop with high socioeconomic importance.

The grape plant will cause poor yield and growth when affected by diseases. The diseases are due to the gray mold, Healthy Grapes, powdery mildew and Sour rot etc. These diseases are judged by the farmers through their experience or with the help of experts through naked eye observation which is not accurate and time-consuming process.

Early detection of disease is then very much needed in the agriculture and horticulture field to increase the yield of the crops. We have proposed a system that can detect and identify diseases in the grape fruits plants.

System Architecture


H/w and S/W requirements


Computer   :   System.

Ram           :    1GB

Rom           :    32GB



Technology    :    Machine Learning.

Front End          :     GUI-tkinter.

IDLE                   :      python 3.10.4

Virtual  Envs     :     Anaconda

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