deletions | additions       diff --git a/subsection_Measuring_the_Infestation_Level__.tex b/subsection_Measuring_the_Infestation_Level__.tex  index 53be46b..d16b78a 100644  --- a/subsection_Measuring_the_Infestation_Level__.tex  +++ b/subsection_Measuring_the_Infestation_Level__.tex 
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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.   After labeling the clusters, they were transformed into feature vectors to be used in training the classifier. We tested on two sets of feature vectors, one composed of the average values of red, green, and blue ($\mu_r$, $\mu_g$, $\mu_b$ in the RGB color space), and the other composed of the average values of a a*  and b b*  ($\mu_{a*}$, $\mu_{b*}$ in the L*a*b* color space), both taken over all the pixels within the cluster. They are basically the centroids of the clusters represented in two different color spaces.  Lastly, the infestation index assigned to the fruit is measured by the ratio of the area of the disease to the area of the fruit divided by because only one side of the cacao pod can be captured by an image. The area of disease and area of the fruit is estimated by number of infected pixels associated with the disease and the number of pixels of the cacao respectively.