## 2 Literature Review
The fuzzy logic architecture contains all the rules and the if-then conditions offered by the human experts to control the decision-making system. [PERSON] et al. mentioned in their study that fuzzy logic was invented by [PERSON], who observed that, unlike computers, humans have a different range of possibilities between \"YES\" and \"NO\" [29]. For this reason, [PERSON] et al. were able to use multi class support vector machines (SVM) with K-means clustering for the classification of diseases with 90% of accuracy, and fuzzy logic to compute the degree of orange disease severity [29]. Other researchers such as [PERSON] et al. proposed an image processing-based approach to automatically grade the disease spread on plant leaves by employing fuzzy logic [30]. [PERSON], [PERSON], and [PERSON] used Matlab to perform K-means based segmentation and classification, percentage infection calculation, and disease grading using the fuzzy logic toolbox [31]. The clustering method of thresholding called the Otsu method, is based on selecting a threshold value for separating the image into two classes such that the variance within each class is minimized. The selection of a threshold value modifies the spread of the two parts of the distribution while the distributions cannot be changed for obvious reasons. The key is to select a threshold that minimizes the combined spread. \begin{table}
\begin{tabular}{c c} \hline
**Methods Used for Plant Disease Severity Prediction** & **Study Summary** \\ \hline Using deep learning to automatically to automatically predict plant disease severity. & [PERSON], [PERSON] and [PERSON] developed different deep learning models that also included the VGG-16 to detect plant severities in four stages. The VGG-16 surpassed the other models with a validation accuracy of 90.4\% [1]. \\ \hline Use of image processing to measure leaf disease symptoms. & In this study, the segmentation of the leaf area was performed by a simple threshold method, and that of the lesion region area with the triangle threshold method. The quotient of lesion area and leaf area were used to categorize the diseases with testing accuracy of 98.60\% [24]. \\ \hline Use of digital image processing to measure leaf disease symptoms. & [PERSON] also used image processing and measurements to detect severities of plant leaf diseases. Using his method, he received an accuracy of 96\% to detect the severities of the plant leaf diseases he dealt with [25]. However, his method also has a disadvantage of limiting users who are not scientists or familiar with image processing. \\ \hline Plant disease incidence and severity measurements by use of machine learning. & [PERSON] and [PERSON] used machine learning algorithms such as support vector machines and k nearest neighbours to detect the plant disease incidents and severity measurements. In their work, they used traditional machine learning algorithms that required handcrafted feature extraction. The handcrafted feature extraction algorithms they used for colour extraction in the images were SURF, SIFT, OBR and HOG [26]. \\ \hline Segmentation of the affected area & The proposed work used two cascaded classifiers. Using local statistical features, the first classifier segmented the leaf from the background. Then, using hue and luminance from the Hue-Saturation-Value colour space, another classifier was trained to detect disease and its stage of severity [27]. \\ \hline Image segmentation and colour sensing & Various new parameters, namely disease severity index (DSI), infection-per region (IPR), and disease-level parameter (DLP) for measuring the disease severity level and level-classification were formulated and derived [28]. \\ \hline \end{tabular}
\end{table} 
Table 2: A summary of methods for plant disease severity detection.