TITLE:
Application of Machine Learning Techniques for Okra Shelf Life Prediction
AUTHORS:
Iveren Blessing Iorliam, Barnabas Achakpa Ikyo, Aamo Iorliam, Emmanuel Odeh Okube, Kenneth Dekera Kwaghtyo, Yahaya I. Shehu
KEYWORDS:
Machine Learning, Shelf Life, Okra, Prediction
JOURNAL NAME:
Journal of Data Analysis and Information Processing,
Vol.9 No.3,
July
20,
2021
ABSTRACT: The ability of machine learning techniques to make
accurate predications is increasing. The aim of this work is to apply machine
learning techniques such as Support Vector Machine, Naïve Bayes, Decision Tree,
Logistic Regression, and K-Nearest Neighbour algorithms to predict the shelf
life of Okra. Predicting the shelf life of Okra is important because Okra becomes harmful for human
consumption if consumed after its shelf life. Okra parameters such as weight
loss, firmness, Titrable Acid, Total Soluble Solids, Vitamin C/Ascorbic acid content, and PH were used
as inputs into these machine learning techniques. Support Vector Machine, Naïve
Bayes and Decision Tree each accurately predicted the shelf life of Okra with
accuracies of 100%. However, the Logistic Regression and K-Nearest Neighbour
achieved 88.89% and 88.33% accuracies, respectively. These results showed that
machine learning techniques especially Support Vector Machine, Naïve Bayes and
Decision Tree can be effectively applied for the prediction of Okra shelf life.