Grading and profiling for export quality coffee beans using red green blue analysis, blob analysis, Hu’s Moments, and back-propagation neural network

Abstract

The Philippines used to be one of the prime exporters of coffee from different parts of the world. However due to lack of technology and the absence of standard the production and exportation of coffee diminish through the years. Until now, the coffee farmers are relying on manual operations of classifying and profiling coffee beans intended to level and match the global standard. Hence, the researchers created a system that will automatically classify and profile coffee beans without human intervention based on the different features of coffee beans using integrated image processing algorithms. The focus of this research is to create a device that can evaluate the size, quality, and roast level of a batch of the coffee beans through the use of image processing techniques and Back Propagation Neural Network. To determine these features, BPNN would serve as the method to develop the brain of the device. The integrated processing algorithms used in this research include K-mean shift, Blob, and Canny Edge to extract the features of the coffee beans and Red Green Blue Analysis, Hu's Moment, and Blob Analysis to make use of these features and feed it into the BPNN. Based on the standard set by the Philippine Coffee Board Inc., the prototype in this research was able to classify and profile different coffee beans with up to 100% accuracy.