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Khaing, Z.M., Naung, Y. and Htut, P.H. (2018) Development of Control System for Fruit Classification Based on Convolutional Neural Network. 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, St. Petersburg, 29 January-1 February 2018, 1805-1807.
https://doi.org/10.1109/EIConRus.2018.8317456

has been cited by the following article:

• 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.