MACHINE LEARNING CLASSIFIER FOR DALIUM GUINEENSE FRUIT USING ITS PHYSICAL PROPERTIES

Authors
Keywords:
machine learning, classifier, classification,, deshelled fruit, whole fruit,, Dalium       Guineense, classification learner.
Abstract

Dalium guineense (DG) is a wild fruit with a brittle epicarp that may be broken accidentally or intentionally while processing during any of the unit operations thereby creating a binomial mixture. Having a binomial mixture of similar items that need to be separated for processing or storage purposes presents a common challenge. This research aims at selecting an appropriate machine learning classifier for the classification of DG fruits. Fifteen measured physical characteristics of randomly selected 200 DG fruits were obtained. Fifty percent of the fruits were deshelled, while the remaining 50% were whole fruits. Different machine learning classifiers were chosen from Decision Tree (DT), Support Vector Machines (SVM), and K-Nearest Neighbor (KNN) classification models, using the classification learner toolkit of MATLAB. The results revealed that Coarse Gaussian SVM and Cosine KNN presented an outstanding classification accuracy of 98.5% compared to other classifiers under investigation. The two classifiers also attained precision, sensitivity, specificity, and F-scores of 99.0%, 98.0%, 99.0%, and 98.5% respectively. The method deployed in this study demonstrates superiority to those reported in some literature. This research recommends the adoption of either the coarse Gaussian SVM or the cosine KNN as the most appropriate classifier for the DG fruits classification.

 

 

Author Biographies
  1. George, U. D, University of Uyo

    Department of Computer Science

  2. Akpan, M. G, University of Uyo

    Department of Agricultural and Food Engineering

  3. Onwe, D. N, University of Uyo

    Department of Agricultural and Food Engineering

References

Abiodun, O. A., Dauda, A. O., Adebisi, T. T., and Alonge, C. D. (2017). Physico-chemical,

microbial and sensory properties of kunu zaki beverage sweetened with black velvet

tamarind (Dialium guineense). Croatian Journal of Food Science and Technology,

Afolabi, O. B., Oloyede, O. I., Ojo, A. A., Onasanya, A. A., Agunbiade, S. O., Ajiboye, B.

O., Jonathan, J., and Peters, O. A. (2018). In vitro antioxidant potential and

inhibitory effect of hydroethanolic extract from African black velvet tamarind

(Dialium indium) pulp on type 2 diabetes linked enzymes. Potravinarstvo, 12(1).

Asoegwu, S. N., Ohanyere, S. O., Kanu, O. P., and Iwueke, C. N. (2006). Physical properties

of African oil bean seed (Pentaclethra macrophylla). Agricultural Engineering

International: CIGR Journal E.Journal. Manuscript FP05 006. Vol. VIII.

Asoiro, F. U., Ezeoha, S. L., Ezenne, G. I., and Ugwu, C. B. (2017). Chemical and

mechanical properties of velvet tamarind fruit (Dialium guineense). Nigerian Journal

of Technology, 36 (1), 252–260.

Battineni, G., Chintalapudi, N., and Amenta, F. (2019). Machine learning in medicine:

Performance calculation of dementia prediction by support vector machines

(SVM). Informatics in Medicine Unlocked, 16, 100200.

Bhambri, P., Dhanoa, I. S., Sinha, V. K., and Kaur, J. (2020). Paddy Crop Production Analysis Based on SVM and KNN Classifier. International Journal of Recent Technology and Engineering, 8(5), 2790–2793.

Bhavani, B. G., Kumar, G. L. N. V. S., Moram Lakshim, Rekha, M. L., K N V P S, and Ramesh, K. N. V. P. S. B. (2021). Prediction of Various Crops in Agricultural Field Using Decision Tree and Naviebayes Algorithm in Machine Learning. International Journal of Engineering Research & Technology, 9(5), 79–83.

Jijo, B. T., and Abdulazeez, A. M. (2021). Classification Based on Decision Tree Algorithm for Machine Learning. Journal of Applied Science and Technology Trends, 02(01), 20–28.

Jye, K. S., Manickam, S., Malek, S., Mosleh, M., and Dhillon, S. K. (2018). Automated plant

identification using artificial neural network and support vector machine. Frontiers in

Life Science, 10(1), 98–107.

Kalichkin, V. K., Alsova, O. K., and Maksimovich, K. Y. (2021). Application of the decision tree method for predicting the yield of spring wheat. IOP. Conference Series: Earth and Environmental Science. (Vol. 839. No 3) IOP Publishing.

Karthikeya, H. K., Sudarshan, K., and Shetty, D. S. (2020). Prediction of Agricultural Crops using KNN Algorithm. International Journal of Innovative Science and Research Technology, 5(5), 1422–1424.

K?l?çkan, A., and Güner, M. (2008). Physical properties and mechanical behavior of olive fruits (Olea europaea L.) under compression loading. Journal of Food Engineering, 87(2), 222–228.

Kramar, V. A, Alchakov, V. V., Dushko, V. R., and Kramar, T. V. (2018). Application of

support vector machine for prediction and classification. Journal of Physics

Conference series (Vol. 1015, No. 3) IOP Publishing

Lasekan, O., and See, N. S. (2015). Key volatile aroma compounds of three black velvet

tamarind (Dialium) fruit species. Food Chemistry, 168, 561–565.

Macuacua, J. C., Centeno, J. A.S. and Amisse, C. (2023). Data mining approach for dry bean

seeds Classification. Smart Agricultural Tech. 5, 100240.

Mohamed, A. E. (2017). Comparative Study of Four Supervised Machine Learning Techniques for Classification. International Journal of Applied Science and Technology, 7(2), 5–18.

Obi, O. F., and Offorha, L. C. (2015). Moisture-dependent physical properties of melon (Citrullus colocynthis lanatus) seed and kernel relevant in bulk handling. Cogent Food & Agriculture, 1(1), 1020743.

Okudu, H. O., Umoh, E. J., Ojinnaka, M. C., and Chianakwalam, O. F. (2017). natritional functional and sensory attributes of jam from velvet tamarind pulp. African Journal of Food Science, 11(2), 44-49.

Olamide, K., Oludele, A., Monday, E., K., and Chigozirim, A. (2020). Evaluation of Decision

Tree Algorithms in Precision Agriculture. International Journal of Computing and

Technology, 7(3), 25-33.

Onwe, D. N., Umani K.C., Olusunde, W. A and Ossom, I. S. (2020). Comparative analysis of

moisture-dependent physical and mechanical properties of African star apple

(Chrysophyllum albidum) seeds relevant in engineering design. SCientific African, 8,

e00303.

Samakradhamrongthai, R.S., and Jannu, T. (2021). Effect of Stevia, xylitol, and corn syrup in

the development of velvet tamarind ( Dalium indum L.) chewy candy. Food

Chemistry, 352, 129353

Singh, L., Janghel, R. R., and Sahu, S. P. (2021). Classification of Hepatic Disease Using

Machine Learning Algorithms. In Advances in Biomedical Engineering and

Technology. Select Proceedings of ICBEST 2018 (pp. 161-173) Springer Singapore.

Song, Y., Huang, J., Zhou, D., Zha, H., & Giles, C. L. (2007). IKNN: Informative k-nearest

neighbor pattern classification. In Knowledge Discovery in Databases: PKDD 2007:

11th European Conference on Principles and Practice of Knowledge Discovery in

Databases, Warsaw, Poland, September 17-21, 2007. Proceedings 11 (pp. 248-264).

Springer Berlin Heidelberg.

Song, Y., and Lu, Y. (2015). Decision tree methods: Applications for classification and

prediction. Shanghai Archives of Psychiatry, 27(2), 130–135.

Syahminan, S., Maknunah, J., Dijaya, R., and Hindarto, H. (2019). KNN (K-Nearby

Neighbor) for identifying agricultural land. In Journal of Physics Conference Series

(Vol. 1402, No 6, p 066059). IOP publishing

Thai, L. H., Hai, T. S., and Thuy, N. T. (2012). Image Classification using Support Vector

Machine and Artificial Neural Network. International Journal of Information

Technology and Computer Science, 5, 32–38.

https://www.mathworks.com/help/stats/choose-a-classifier.html Retrieved 10 February, 2023

Cover Image
Downloads
Published
2023-05-31
Section
Articles
License

Copyright (c) 2023 FUTA JOURNAL OF ENGINEERING AND ENGINEERING TECHNOLOGY

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Copyright

With the submission of a manuscript, the corresponding author confirms that the manuscript is not under consideration by another journal. With the acceptance of a manuscript, the Journal reserves the exclusive right of publication and dissemination of the information contained in the article. The veracity of the paper and all the claims therein is solely the opinion of the authors not the journal.

How to Cite

MACHINE LEARNING CLASSIFIER FOR DALIUM GUINEENSE FRUIT USING ITS PHYSICAL PROPERTIES. (2023). FUTA JOURNAL OF ENGINEERING AND ENGINEERING TECHNOLOGY, 17(1), 90-101. https://doi.org/10.51459/futajeet.2023.17.1.571

Similar Articles

41-50 of 133

You may also start an advanced similarity search for this article.