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\section{Conclusion and Recommendation}  In summary, we used the K-Means algorithm to group the pixels into healthy and infected clusters. The clusters were then labeled and used to train an SVM classifier that would automatically determine which clusters contain infected pixels and which clusters contain healthy pixels.  Based on the criteria described above, a bigger $k$ would give better results, on both K-Means image segmentation and SVM classification. This is because increasing the number of clusters lessens the variance within clusters, thus, decreasing the chances of mixing healthy and infected pixels in the same cluster. However, more clusters would mean additional computational complexity and longer processing times. K-Means alone has a computational complexity of $O(n^{dk+1}\log{}n)$ \cite{inaba1994applications}, where $n$ refers to the number of samples, $k$ refers to the number of clusters, and $d$ refers to the number of dimensions. This implies that the complexity increases exponentially as the number of clusters increase. Our experiments show that using 4 clusters gives a good balance between segmentation performance and computational complexity. As shown in Figure \ref{fig:accuracyxk}, the accuracy starts to plateau after 4 clusters, i.e. increasing the number of clusters would only give a small increase in accuracy. accuracy while exponentially increasing complexity.  An extension of this research can include a background subtraction algorithm to automate the process of removing the backgrounds. Other clustering algorithms and classification algorithms may also be explored to improve performance in terms of accuracy and processing time.