Comparison of Performance of Some Classification Methods to Evaluate the Quality of Vegetables from its Morphology

Abstract

One important aspect of Data Science is its ability to classify subjects into non-overlapping groups based on one or several input variables. Several methods and algorithms are available in the literature for classifying subjects based on the values of multiple observed variables. Such classification tools are Naive Bayesian Classifiers, Logistic Regression, Discriminant Analysis, k-nearest neighbourhood etc. This paper attempts to recognise if the morphological variables, identified either through literature review or from expert opinion, can be utilised to understand the quality of vegetables. Consequently, the current researchers obtained primary data about the morphology of the vegetables through experimentation. The outcome variable is the quality of the vegetables classified as eatable or not-eatable because of worm attack. Several classification methods are then compared for the classification exercise by building the model based on the training sample and testing the performance of the models in the holdout sample. Methods of classification performance statistics like sensitivity, specificity, precision etc. are used for their comparison. The study finds that Naive Bayes and Logistic Regression models perform better for this classification exercise. For example, only eggplant (brinjal) is considered for the study.

Keywords

• Machine learning
• classification
• data science
• morphology of vegetables

References

1. Ulger TG, Songur AN, Cirak O and Cakiroglu FP, “Role of Vegetables in Human Nutrition and Disease Prevention”, in Vegetables-Importance of Quality Vegetables to Human Health, intechopen, 2018, pp. 7-32; doi: 10.5772/intechopen.77038.
2. Gowda LR, “Genetically Modified Aubergine(Also Called Brinjal or Solanum melogena)” in Genetically Modified Organisms in Food, (2016), 27-37; doi: 10.1016/B978012802259700004-X.
3. S. Herbst, "The New Food Lover's Companion: Comprehensive Definitions of Nearly 6,000 Food, Drink, and Culinary Terms. Barron's Cooking Guide," Hauppauge, NY : Barron's Educational Series. ISBN 0764112589.
4. Y. Noda , T. Kaneyuki, K. Igarashi and A. Mori, "Antioxidant activity of nasunin, an anthocyanin in eggplant peels," Toxicology, pp. 119-123, 2000.
5. B. Whitaker and J. Stommel, "Distribution of Hydroxycinnamic Acid Conjugates in Fruit of Commercial Eggplant (Solanum melongena L.) Cultivars," Journal of Agricultural Food Chemistry, vol. 51, pp. 3448-3454, 2003.
6. A. Minhas, "Production volume of vegetables India FY 2008-2022," 22 03 2023. [Online]. Available: http://www.statista.com.
7. AVRDC Eggplant entomology, "Control of eggplant fruit and shoot borer.Progress Report," Asian Vegetable Research and Development Center,(AVRDC), Shanhua,Taiwan, 1994.
8. E. A. Netam M ., "Screening of shoot and Fruit Borer(Leucinodes orbonalis Guenee) for Resistance in Brinjal (Solanum melongena L.) Germplasm Lines," Inernational Journal of current Microbiology and Applied Sciences 7.2, pp. 3700-3706, 2018.

9. R. H. Ajaz and L. Hussain, "Seed Classification using Machine Learning Techniques," Journal of Multidisciplinary Engineering Science and Technology(JMEST), pp. 1098-1102, 2015.
10. Manoj Kumar D P, Malyadri N, Srikanth MS, Ananda Babu J, “A Machine Learning model for Crop and Fertilizer recommendation”, Natural Volatiles & Essential Oils, 8(5) (2021) 10531-10539.
11. Padao F.R.F. & Maravillas E. A. “Using Naive Bayesian method for plant Leaf classification based on shape and texture featutres” 2015 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control,Enviroment Management(HNICEM), 2015.
12. S. Bishnoi, N. A. Ansari, M. Khan, S. Heddam and A. Malik, "Classification of Cotton Genotypes with Mixed Continuous and Categorical Variables: Application of Machine Learning Models," Sustainability, pp. 14,13685, 2022.
13. A. P. &. D. S. S. Gawande, "Implementation of fruit grading system by image processing and data classifier-a review," International Journal of Engineering Research and General Science, vol. 2, no. 6, pp. 411-413, 2014.
14. S. C. Lauguico, R. I. S. Cocepcion, J. D. Alejandrino, R. R. Tobias and E. P. Dadios, "Lettuce life stage classification from texture attributes using machine learning estimators and feature selection processes," International Journal of Advances in Intelligent Informatics, pp. 173-184, 2020.
15. I. ,. Iorliam, " Application of Machine Learning Techniques for Okra Shelf Life Prediction," Journal of Data Analysis and Information Processing, pp. 136-150, 2021.
16. Davies T, Yu Louie JC, Ndanuko R, Barbieri S, Perez-Concha O, H Y Wu J, “A Machine Learning Approach to Predict the Added-Sugar Content of Packaged Foods”, The Journal of Nutrition 152(1), (2022) 343-349.
17. K. Krishnaiah and O. Vijay, "Evaluation of brinjal varieties for resistance to shoot and fruit borer," Indian J Hort, pp. 84-86, 1975.
18. R. Garewal and D. Singh, "Fruit characters of brinjal in relation to infestation by Leucinodes orbonalis Guen," Indian J Ent, vol. 57, pp. 336-343, 1995.
19. P. Hazra, R. Dutta and T. Maity, "Morpholological and Biochemical characters associated with field tolerence of brinjal (Solanum melongena L.) to shoot and fruit borer and their implication in breed for tolerence," Indian Journal Genet, pp. 255-256, 2004.
20. E. Fix and J. L. Hodges, "Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties," USAF School of Aviation Medicine, Randolph Field, Texas, 1951.
21. T. M. Cover and P. E. Hart, " "Nearest neighbor pattern classification"," IEEE Transactions on Information Theory, p. 13 (1): 21–27, 1967.
22. N. a. N. D. a. T. P. Ali, "Evaluation of k-nearest neighbour classifier performance for heterogeneous data sets," SN Applied Sciences, 2019.
23. H. Zhang, "The Optimality of Naive Bayes," Proceedings of the Seventeenth International Florida Artificial Intelligence .
24. P. A. a. L. N. Flac, "Naive Bayesian Classification of Structured Data," Machine Learning, Boson: Kluwer Academic Publisher, pp. 1-37, 2004.
25. A. W. Manage, " classification of all rounders in limited over cricket - a machine learning approach," Journal of Sports analytics, pp. 6(4),295-306, 2020.
26. E. Hunt, J. Marin and P. Stone, "Experiment in induction academic press," N.Y., p. 247, 1966.
27. L. Breiman, J. Friedman, R. Olshen and C. Stone , "Classification and Regression Trees," Chapman Hall/ CRC Press: New York, NY, USA, 1984.
28. L. Breiman, "Random Forests," Machine Learning, vol. 45, pp. 5-32, 2001.
29. J. Ali, R. Khan and N. Ahmad, "Random Forests and Decision Trees," IJCSI International Journal of Computer Science Issues, vol. 9, no. 5, pp. 272-278, 2012.
30. M. Kubat, R. Holte and S. Matwin, "Machine learning for the detection of oil spoills in satellite radar images.," Mach. Learn, vol. 30, pp. 195-215, 1998.