Fruit Disease Detection Using Color,Texture and ANN

Project Code :TCMAPY360

Objective

Our goal is to detect the fruit diseases by using OpenCV library, ANN model and K-means clustering method. Such an application is immensely useful in the field of farming and it is inexpensive for the user. We will also use image processing techniques for implementation.

Abstract

Now-a-days as there is prohibitive demand for agricultural industry, effective growth and improved yield of fruit is necessary and important. But manual monitoring will not give satisfactory result all the times and they always need satisfactory advice from expert. So it requires proposing an efficient smart farming technique which will help for better yield and growth with less human efforts. 

Traditional system uses thousands of words which lead to boundary of language. Whereas system that we have come up with, uses image processing techniques for implementation as image is easy way for conveying. In the proposed work, OpenCV library is applied for implementation. K-means clustering method is applied for image segmentation, the images are catalogue and mapped to their respective disease categories on basis of four feature vectors colour, morphology, texture and structure of hole on the fruit. Artificial Neural Network (ANN) concept is used for pattern matching and classification of diseases.


keywords: OpenCV, K-means clustering, SURF, Artificial Neural Network.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

HARDWARE SPECIFICATIONS: 

  • Processor- I3/Intel Processor
  •  RAM- 4GB (min)
  •  Hard Disk- 128 GB 
  • Key Board-Standard Window Keyboard.
  •  Mouse-Two or Three Button Mouse. 
  • Monitor-Any. 

SOFTWARE SPECIFICATIONS:

  •  Operating System: Windows 7+ 
  • Technology: Python 3.6+
  •  Server side scripts: HTML, CSS, JS 
  • IDE: PyCharm IDE
  •  Frame works: Flask Libraries Used: Pandas, NumPy, OpenCV, TensorFlow, Matplotlib.

Learning Outcomes

  • Scope of real time application scenarios.
  • What is a search engine and how browser can work.
  • What type of technology versions?
  • Need of PyCharm-IDE to develop a web application.
  • How to implement segmentation.
  • Where this application can be used.
  • What are the diseases attacked by the fruits.
  • Features of OpenCV.
  • Working Procedure.
  • Testing Techniques.
  • How to run and deploy the applications.
  • Introduction to basic technologies.
  • How project works.
  • Input and Output modules.
  • How test the project based on user inputs and observe the output.
  • Project Development Skills:
    • Problem analyzing skills.
    • Problem solving skills.
    • Creativity and imaginary skills.
    • Programming skills.
    • Deployment.
    • Testing skills.
    • Debugging skills.
    • Project presentation skills.
    • Thesis writing skills.

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