Development of Crystal Mungbean Quality Analysis Model using HAAR Cascade and Local Binary Pattern (LBP) Histogram

Abstract

Mungbean or "Vigna Radiata" is an important food legume that can substitute rice or corn. The Municipality of San Mateo in Isabela is one of the exporters and contributors of Mungbean in the Philippines. The Municipality of San Mateo is entitled the Mungbean Capital of the Philippines. In identifying grain type and quality, the visual inspector manually inspects the grains, which is tedious and sometimes inaccurate. The visual inspectors have high labor costs, fatigue, inconsistency, variability, and non-availability of skilled workers. That is why this study focuses on developing Crystal Mungbean Quality Analysis Model using HAAR Cascade and Local Binary Pattern (LBP) Histogram. This study specifically aimed the following: (a) to develop a dataset for the Crystal Mungbean Quality Analysis Model, (b) to
develop a predictive tool for detecting the quality of Crystal Mungbean, and (c) to compare the actual result of the visual inspector and predictive model in terms of Accuracy. Several Interviews, Observations, and Experimentations were conducted in this study. The researcher used the Rapid Application Model as the Study's Conceptual Framework. Based on the Summary of findings, the researcher found out that the Crystal Mungbean Quality Analysis Model accurately gave results using the developed Predictive Tool.