Enhancement of Wheat Leaf Images Using Fuzzy-Logic Based Histogram Equalization to Recognize Diseases

Abstract

The detection of diseases affecting wheat is very important as it relates to the issue of food security, which poses a serious threat to human life. Recently, farmers have heavily relied on modern systems and techniques for the control of the vast agricultural areas. Computer vision and data processing play a key role in detecting diseases that affect plants, depending on the images of their leaves. In this article, Fuzzy- logic based Histogram Equalization (FHE) is proposed to enhance the contrast of images. The fuzzy histogram is applied to divide the histograms into two subparts of histograms, based on the average value of the original image, then equalize them freely and independently to conserve the brightness of the image. The proposed method was evaluated using two well-known parameters: Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). The best results were reflected by MSE = 0.071 and PSNR =39.58 for the Mildew Powdery disease. It is impressive to recognize that the proposed method yielded clear positive outcomes through producing better contrast enhancement while preserving the details of the original image, as confirmed by the subjective metrics.