deletions | additions       diff --git a/subsection_Measuring_the_Infestation_Level__.tex b/subsection_Measuring_the_Infestation_Level__.tex  index ff2396b..5a96486 100644  --- a/subsection_Measuring_the_Infestation_Level__.tex  +++ b/subsection_Measuring_the_Infestation_Level__.tex 
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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 in the RGB color space, and the other composed of the average values of a* and 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 level $I$ assigned to the fruit is measured by the ratio of the area of the disease $A_d$ to the area of the fruit $A_f$ divided by 2 (See Equation ). \ref{eq:infLevel}).  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. The adjustment factor $\frac{1}{2}$ is based on the reason that only one side of the cacao pod can be captured by an image. $$\label{eq:infLevel} I = \frac{A_d}{2A_f} × 100 (\%)$$