Non-Invasive Transport Tier Classification of Banana ‘Señorita’ (Musa Acuminata) Using Machine Learning Techniques
Robert G. de Luna, Verna C. Magnaye, Rose Anne L. Reaño, Karina L. Enriquez, Meghann Kim O. Dungca, Darlene Rocel C. Filler, Cailon Jullius V. Cabrera, Charlene M. Reyes, Seane Allen J. Saballa, Nicole Anne O. Maligalig
Abstract
The lack of a transport quality forecasting system in farming and sorting facilities of indigenous varieties of bananas is aiding the increase of food waste generation in the country. This in turn decreases agriculture sustainability and imposes economic losses to farmers. Musa acuminata ‘Señorita’ are diploid cultivars of bananas originating in the Philippines. This study aims to develop a machine learning-based system that classifies Musa acuminata ‘Señorita’ bananas into their transport tiers: (I) for interprovincial distribution, (II) for intra provincial distribution, or (III) subject for rejection. The model is trained and went through 7 machine learning classifiers to identify which model is the most compatible with the system design. The application of external parameters such as size, girth, weight, maturity stage, and RGB parameters can be the foundation to develop a machine learning-based banana transport tier classifier that accurately monitors and determines how long they can travel based on their maturity level. Among the seven models, Logistic Regression, Linear Discriminant Analysis, Decision Tree Classifier, Gaussian Naive Bayes, and Support Vector Machine attained a classification accuracy of 100 %. The development of this system can aid in the proper distribution of produce and help uplift the country's agriculture and economic sustainability.