Cacao Bean Quality Assessment Procedure: A Method for Classification Process
Journal Title: Journal of Computing and Innovation - Year 2018, Vol 2, Issue 2
Abstract
The study focus on Cacao Bean Quality Assessment. The method starts on image processing of sample cacao bean to detect the defected beans on the sample submitted for quality assessment. After the Image Processing, this paper proposes to utilize the Adaptive Neural-Fuzzy Inference System (ANFIS) technique for classification of the cacao bean quality level. The ANFIS technique serves as the medium for identifying the quality level of the cacao bean sample submitted for evaluation and categorization. The input variables are Bean Count, Moldy, Slaty, and Defected Beans. The Output Grade level of Cacao. The Two hundred (200) data sets used were taken from the image processing output and normalized to be trained in ANFIS using the tool in Matlab, Eighty (80) percent for training and Twenty (20) percent for checking. During the training with the use of Matlab software, it resulted in an accuracy rate value of 99.715% with an error rate of 0.285%. The study shows that ANFIS is a technique that can be used efficiently to classify the cacao bean quality based on their category level.
Authors and Affiliations
Dindo C. Obediencia, Deborah Go Brosas, Ritchell Solitario Villafuerte
Keywords
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