deletions | additions       diff --git a/subsection_Measuring_the_Infestation_Level__.tex b/subsection_Measuring_the_Infestation_Level__.tex  index 1d25807..6158e7d 100644  --- a/subsection_Measuring_the_Infestation_Level__.tex  +++ b/subsection_Measuring_the_Infestation_Level__.tex 
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After clustering, the image is then segmented based on the clusters formed, i.e. each cluster of pixels form a separate image. The general idea is that the infected part of the fruit would be similar in color and will tend to be in a separate cluster from the healthy part of the fruit.  Afterwards, manual labeling of formed clusters were done  due to the limitations of the clustering algorithm to automatically identify which pixels are infected and which are not, manual labeling of formed clusters were done. not.  Then,the  automating the labeling process was now viewed as a classification problem. For the next step, we used a Support Vector Machine (SVM) to classify infected pixels from healthy pixels. Support Vector Machine is a supervised machine learning algorithm used for classification and regression tasks. 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.