deletions | additions       diff --git a/subsection_Identifying_the_Infected_Clusters__.tex b/subsection_Identifying_the_Infected_Clusters__.tex  index bf83e17..6fe2aba 100644  --- a/subsection_Identifying_the_Infected_Clusters__.tex  +++ b/subsection_Identifying_the_Infected_Clusters__.tex 
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The function of the K-Means algorithm is to cluster similar pixels together. By doing so, it is able to separate the infected pixels from the healthy pixels. However, the algorithm is oblivious as to which clusters contain infected pixels and which clusters contain healthy pixels. This information is necessary for the computation of the infestation level. The problem now changes from a segmentation problem to a classification problem.  Training a classifier requires a set of features that represent the data points and discriminate between their classes. In this case, the data points are the clusters of pixels obtained from the K-Means algorithm. We observed that humans rely on color to distinguish the infected part of the fruit. Therefore, it is logical to choose color as the main feature for the classifier. We transformed labeled  the clusters infected and healthy based on the pixels contained in them. They are then transformed  into feature vectors to be used in training the classifier. The feature vector is composed of the average values of red, green, and blue ($\mu_r$, $\mu_g$, $\mu_b$ in the RGB color space) taken over all the pixels within the cluster. We used Support Vector Machine for our classifier. Support Vector Machine is a supervised machine learning algorithm used for classification and regression tasks. .....