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.

Preprocessing of leaf images using brightness preserving dynamic fuzzy histogram equalization technique

“…A histogram equalization technique based on the fuzzy logic is recommended to improve the contrast of the input images [17]. This method is assessed using the metrices namely, mean square error (MSE) and peak signal to noise ratio (PSNR).…”

Enhancement Infrared-Visible Image Fusion Using the Integration of Stationary Wavelet Transform and Fuzzy Histogram Equalization

“…FHE is divided into two operations: first, counting the fuzzy histogram based on fuzzy set theory to handle the intensity values in the best way. Second, partitioned the fuzzy histogram from the first operation is into two subhistograms depending on the median value of the source image and then each sub is equalized in an independent form to preserve the brightness values of the image [21]. FHE can be considered as a set of real numbers based on fuzzy membership function as his(i) and i∈ {0,1, … , 𝑘 − 1} and can be in a mathematical expression as mentioned in [22]:…”