Image Quality Enhancement for Wheat Rust Diseased Images Using Histogram Equalization Technique

Also Available Domains Deep Learning

Project Code :TMMAIP397


In this study, different types of image enhancement techniques like histogram equalization and CLAHE are discussed to enhance the wheat plant image.


Wheat is the most significant crop on the planet in terms of agriculture. It is a winter cereal crop that accounts for 14% of global food output. Wheat is a necessary component of everyone's diet. The goal of this project is to improve the quality of wheat crop pictures in the agriculture sector. The images acquired in a real-time context may not always be clear enough to detect the disease in the crop. As a result, the photos must be enhanced. Histogram features (statistical features) are retrieved in this work to aid in the recognition of wheat rust sick photos. The histogram equalization method is a good way to improve an image's pixel intensity. Moreover, several difficulties to improve image quality, such as the effect of the histogram, histogram equalization, and Contrast Limited Adaptive Histogram Equalization, have been investigated (CLAHE). Also, it is observed that instead of plotting a simple histogram, adaptive histogram equalization is the best way to equalize all pixel values at the same level. Thereafter, the importance of a 3D plot for color distribution is also discussed. It is concluded that adaptive histogram equalization really helps in enhancing the quality of the image and also using 3D plots one can get fine information to estimate the majority of different colors present in the image for performing segmentation and feature extraction.

Keywords: Wheat crop disease, Feature extraction, Histogram equalization, Adaptive Histogram equalization, Image enhancement techniques, RGB and HSV Color space.

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

Block Diagram


Software & Hardware Requirements:

Software: Matlab 2020a or above


Operating Systems:

  • Windows 10
  • Windows 7 Service Pack 1
  • Windows Server 2019
  • Windows Server 2016


  • Minimum: Any Intel or AMD x86-64 processor
  • Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support


  • Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
  • Recommended: An SSD is recommended a full installation of all Math Works products may take up to 29 GB of disk space


  • Minimum: 4 GB
  • Recommended: 8 GB 

Learning Outcomes

·   Introduction to Matlab


·   How to start with MATLAB

·   About Matlab language

·   Matlab coding skills

·   About tools & libraries

·   Application Program Interface in Matlab

·   About Matlab desktop

·   How to use Matlab editor to create M-Files

·   Features of Matlab

·   Basics on Matlab

·   What is an Image/pixel?

·   About image formats

·   Introduction to Image Processing

·   How digital image is formed

·   Importing the image via image acquisition tools

·   Analyzing and manipulation of image.

·   Phases of image processing:

               o  Acquisition

               o  Image enhancement

               o  Image restoration

               o   Color image processing

               o  Image compression

               o   Morphological processing

               o   Segmentation etc.,

·   How to extend our work to another real time applications

·   Project development Skills

               o   Problem analyzing skills

               o   Problem solving skills

               o   Creativity and imaginary skills

               o   Programming skills

               o   Deployment

               o   Testing skills

               o   Debugging skills

               o   Project presentation skills

               o   Thesis writing skills

Demo Video

Request Video

Related Projects

Final year projects