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

Project Code :TCMAPY639

Objective

As images acquired in a real-time context may not always be clear enough to detect the disease in the crop, a histogram equalization technique is used to enhance it.

Abstract

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 equalisation 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 equalisation, and Contrast Limited Adaptive Histogram Equalization, have been investigated (CLAHE). Also, rather than presenting a basic histogram, histogram equalisation is found to be the most effective technique to equalise all pixel values at the same level. In addition, other colour space models, such as RGB and HSV, were used for analysis. Following that, the significance of a 3D plot for colour distribution is explained. It is found that histogram equalisation significantly improves image quality, and that fine information may be obtained from 3D plots to estimate the majority of distinct colours present in the image for segmentation and feature extraction. It's impressive to see that the proposed method produced clear positive results by improving contrast enhancement while keeping the original image's details. The air and soil are both carriers of the leaf rust disease. It costs farmers a lot of money all over the world. This paper describes an automated approach for detecting Leaf rust illnesses that was created by the author and is more accurate and efficient.

 

Keyword: Crop disease in wheat Image enhancement, RGB and HSV colour space, feature extraction, histogram equalisation

 

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

Block Diagram

Specifications

H/W Specifications:

     •Processor :  I5/Intel Processor

     •RAM :  8GB (min)

     •Hard Disk:  128 GB

S/W Specifications:

     •Operating: Windows 10

     •Server-side Script :Python 3.6

     •IDE: PyCharm, Jupyter notebook

     •Libraries Used :Numpy, IO, OS, Flask, Keras, pandas, tensorflow


Learning Outcomes

        Practical exposure to

·         Hardware and software tools

·         Solution providing for real time problems

·         Working with team/individual

·         Work on creative ideas

·         Testing techniques

·         Error correction mechanisms

·         What type of technology versions is used?

·         Working of Tensor Flow

·         Implementation of Deep Learning techniques

·         Working of CNN algorithm

·         Working of Transfer Learning methods

·         Building of model creations

·         Scope of project

·         Applications of the project

·         About Python language

·         About Deep Learning Frameworks

·         Use of Data Science

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