Author(s)

Iveren Blessing Iorliam¹, Barnabas Achakpa Ikyo¹, Aamo Iorliam^2*, Emmanuel Odeh Okube², Kenneth Dekera Kwaghtyo², Yahaya I. Shehu³

¹Centre for Food Technology and Research (CEFTER), Benue State University, Makurdi, Nigeria.
²Department of Mathematics & Computer Science, BSU, Makurdi, Nigeria.
³Shehu Shagari College of Education, Sokoto, Nigeria.

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.

Machine Learning, Shelf Life, Okra, Prediction

Iorliam, I. , Ikyo, B. , Iorliam, A. , Okube, E. , Kwaghtyo, K. and Shehu, Y. (2021) Application of Machine Learning Techniques for Okra Shelf Life Prediction. Journal of Data Analysis and Information Processing, 9, 136-150. doi: 10.4236/jdaip.2021.93009.