Performance of Mung Bean Classification Using Decision Trees: An Image Processing Approach
Keywords:Machine Learning, Image Processing, Decision Trees, Classification
The mung bean (Vigna radiata L.) is one of the most significant edible legume crops which accounts for approximately 6 million hectares of crop area in the Philippines (almost 8.5 percent of global pulse area) and consumed by most Asian families. San Mateo, Isabela is the Philippines' mung bean capital. For several years, farmers in this area have used the munggo after rice planting as a form of crop rotation. This is primarily done to supplement their income and to enrich the soil with nitrogen. Nhaveew technologies become valuable to industries and people in the sectors of agriculture, education, financial institutions, and other essential fields. This study only uses 217 samples for the Chinese Variant, 283 samples for PAGASA-7, and 235 samples for Kulabo. The classification model was successfully created using 5 morphological features. The most accurate result gained the highest accuracy score of 86.3% percent using the Fine tree, and the lowest is 66.4% percent using the Course tree.