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

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, histogram equalization is the best way to equalize all pixel values at the same level. In addition to that, various color spaces models such as RGB and HSV have been utilized for analysis. Thereafter, the importance of a 3D plot for color distribution is also discussed.

It is concluded that 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, Image enhancement, RGB and HSV Color space.