Image quality enhancement for Wheat rust diseased images using Histogram equalization technique

In the agriculture domain, wheat is the most important crop across the world. It is a winter cereal crop that provides 14% food production worldwide. Wheat is an essential food for everyone. The motivation behind this work is to enhance the quality of wheat crop images in the agricultural area. Sometimes, the pictures captured in a real-time environment may not be clear for detecting the disease from the crop. So, there is a need to enhance the images. In this paper, histogram features (statistical feature) are extracted for further recognition of wheat rust diseased images. The histogram equalization is a good approach to enhance the pixel intensity of an image. Moreover, various challenges to enhance the quality of an image have also been explored, such as the effect of the histogram, histogram equalization, and Contrast Limited Adaptive Histogram Equalization (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.