Abstract:
This study tries to apply digital image processing techniques towards
sample Coffee raw quality value grading. More specifically, this study
emphases on comparing performance of classification algorithms to use
for grading coffee raw quality by using image processing methods. To ease
experimentation image processing phases are followed, including image
acquisition, image preprocessing (image filtering and attribute selection),
image analysis (segmentation, feature extraction and classification), and
image understanding for raw quality image grading. Artificial Neural Network, support vector machine and K-Nearest neighbor classifiers on
each classification parameter of morphology, color and the mixture of the
two has been made. Experimental outcomes confirm that Artificial Neural
Network classifier generated the highest performance of 89.45% accuracy as
compared to support vector machine (with 83.75%) and K-Nearest neighbor
classifier (with 77.85%). Thus, suitable selection of image processing and
classification techniques paves the way for higher accuracy in the higher-level
process for decision making.
