Current Science

Open Access Open Access  Restricted Access Subscription Access

Performance of Advanced Machine Learning Models in the Prediction of Amylose Content in Rice Using Internet of Things-Based Colorimetric Sensor


Affiliations
1 ICAR-Krishi Vigyan Kendra, Kandali, Hassan 573 217, India
2 Department of Processing and Food Engineering, College of Agricultural Engineering, University of Agricultural Sciences, Raichur 584 104, India
3 Department of Renewable Energy Engineering, College of Agricultural Engineering, University of Agricultural Sciences, Raichur 584 104, India
4 Pesticide Residue and Food Quality Analysis Laboratory, College of Agricultural Engineering, University of Agricultural Sciences, Raichur 584 104, India
 

Rice ageing is a complicated process that is difficult to examine methodically. Several physicochemical properties of rice change with age as a function of moisture content and storage temperature. Among these qualities, amylose content is the most important and numerous metrics depend on it. Several sensors, Internet of Things, Information and Communication Technology, artificial intelligence and machine learning (ML) approaches are being used in technological interventions to tackle this problem. In the present study, seven advanced ML models were evaluated to classify the different concentrations of amylose using light-intensity data obtained by the novel colorimetric amylose sensor. From the performance of the evaluated ML models, it was observed that for the light intensity dataset obtained from the sensor, higher and similar model parameters and an accuracy value of 0.77 were observed for both artificial neural network (ANN) and k-nearest neighbour (KNN) algorithms, followed by accuracy values of 0.75, 0.74, 0.65, 0.61 and 0.61 respectively, for the decision tree, random forest, AdaBoost, logistic regression and support vector machine algorithms. Thus ANN and KNN are promising in predicting the different classes of amylose in rice.

Keywords

Amylose Content, Artificial Intelligence, Machine Learning, Mathematical Modelling, Rice.
User
Notifications
Font Size

  • Perez, C. M. and Juliano, B. O., Texture changes and storage of rice. J. Texture Stud., 1981, 12(1), 321–333.

  • Faruq, G., Prodhan, Z. H. and Nezhadahmadi, A., Effects of ageing on selected cooking quality parameters of rice. Int. J. Food Prop., 2015, 18(4), 922–933.

  • Zhou, Z., Robards, K., Helliwell, S. and Blanchard, C., Ageing of stored rice: changes in chemical and physical attributes. J. Cereal Sci., 2001, 35(1), 65–78.

  • Devraj, L., Natarajan, V., Ramachandran, S. V., Manicakam, L. and Saravanan, S., Accelerated aging by microwave heating and methods to distinguish aging of rice. J. Food Process Eng., 2020, 43(6), 13405–13415.

  • Popa, A. et al., An intelligent IoT-based food quality monitoring approach using low-cost sensors. Symmetry, 2019, 11(3), 374–391.

  • Moradi, M., Balanian, H., Taherian, A. and Mousavi Khaneghah, A., Physical and mechanical properties of three varieties of cucumber: a mathematical modeling. J. Food Process Eng., 2020, 43(2), 13323–13330.

  • Osisanwo, F. Y., Akinsola, J. E. T., Awodele, O., Hinmikaiye, J. O., Olakanmi, O. and Akinjobi, J., Supervised machine learning algorithms: classification and comparison. Int. J. Comput. Trends Technol., 2017, 48(3), 128–138.

  • Celine, S., Maria Dominic, M. and Savitha Devi, M., Logistic regression for employability prediction. Int. J. Innov. Technol. Exp. Eng., 2020, 9(3), 2471–2478.

  • Anon., AdaBoost Classifier in Python, 2018; https://www.datacamp.com/community/tutorials/adaboost-classifier-python (accessed on 1 May 2021).

  • Anon., Understanding logistic regression in Python, 2019; https:// www.datacamp.com/community/tutorials/understanding-logistic-regression-python (accessed on 1 May 2021).

  • Anon., Understanding random forests classifiers in Python, 2018; https://www.datacamp.com/community/tutorials/random-forests-classifier-python (accessed on 1 May 2021).

  • Liu, H., Zhang, X. and Zhang, X., PwAdaBoost: possible world based AdaBoost algorithm for classifying uncertain data. Knowl.- Based Syst., 2019, 186, 104930.

  • Zheng, J., Lin, D., Gao, Z., Wang, S., He, M. and Fan, J., Deep learning assisted efficient AdaBoost algorithm for breast cancer detection and early diagnosis. IEEE Access, 2020, 8, 96946–96954.

  • Ji, X., Yang, B. and Tang, Q., Acoustic seabed classification based on multibeam echosounder backscatter data using the PSO-BP-AdaBoost algorithm: a case study from Jiaozhou Bay, China. IEEE J. Ocean. Eng., 2020, 46(2), 509–519.

  • Kim, J., Shin, N., Jo, S. Y. and Kim, S. H., Method of intrusion detection using deep neural network. In IEEE International Conference on Big Data and Smart Computing, 2017, pp. 313–316.

  • Arboleda, E. R., Fajardo, A. C. and Medina, R. P., Classification of coffee bean species using image processing, artificial neural network and k-nearest neighbors. In IEEE International Conference on Innovative Research and Development, Bangkok, Thailand, 2018, pp. 1–5.

  • Siddiqui, S. A., Salman, A., Malik, M. I., Shafait, F., Mian, A., Shortis, M. R. and Harvey, E. S., Automatic fish species classification in underwater videos: exploiting pre-trained deep neural network models to compensate for limited labelled data. ICES J. Mar. Sci., 2018, 75(1), 374–389.

  • Narang, A., Batra, B., Ahuja, A., Yadav, J. and Pachauri, N., Classification of EEG signals for epileptic seizures using Levenberg–Marquardt algorithm based multilayer perceptron neural network. J. Intell. Fuzzy Syst., 2018, 34(3), 1669–1677.

  • Stephen, O., Sain, M., Maduh, U. J. and Jeong, D. U., An efficient deep learning approach to pneumonia classification in healthcare. J. Healthcare Eng., 2019; https://doi.org/10.1155/2019/4180949.

  • Adeniyi, D. A., Wei, Z. and Yongquan, Y., Automated web usage data mining and recommendation system using k-nearest neighbor (KNN) classification method. Appl. Comput. Informat., 2016, 12(1), 90–108.

  • Shah, K., Patel, H., Sanghvi, D. and Shah, M., A comparative analysis of logistic regression, random forest and KNN models for the text classification. Augment. Hum. Res., 2020, 5(1), 1–16.

  • Moldagulova, A. and Sulaiman, R. B., Using KNN algorithm for classification of textual documents. In Eighth International Conference on Information Technology, 2017, pp. 665–671.

  • Gupta, B., Rawat, A., Jain, A., Arora, A. and Dhami, N., Analysis of various decision tree algorithms for classification in data mining. Int. J. Comput. Appl., 2017, 163(8), 15–19.

  • Ahmim, A., Maglaras, L., Ferrag, M. A., Derdour, M. and Janicke, H., A novel hierarchical intrusion detection system based on decision tree and rules-based models. In 15th International Conference on Distributed Computing in Sensor Systems, 2019, pp. 228–233.

  • Abdallah, I. et al., Fault diagnosis of wind turbine structures using decision tree learning algorithms with big data. Safety and Reliability – Safe Societies in a Changing World, CRC Press, 2018, pp. 3053–3061.

  • Ohsaki, M., Wang, P., Matsuda, K., Katagiri, S., Watanabe, H. and Ralescu, A., Confusion matrix-based kernel logistic regression for imbalanced data classification. IEEE Trans. Knowl. Data Eng., 2017, 29(9), 1806–1819.

  • De Menezes, F. S., Liska, G. R., Cirillo, M. A. and Vivanco, M. J., Data classification with binary response through the boosting algorithm and logistic regression. Expert Syst. Appl., 2017, 69, 62–73.

  • Dumitrescu, E., Hue, S., Hurlin, C. and Tokpavi, S., Machine learning for credit scoring: improving logistic regression with non-linear decision-tree effects. Eur. J. Oper. Res., 2021, 297(3), 1178–1192.

  • Mathew, J., Pang, C. K., Luo, M. and Leong, W. H., Classification of imbalanced data by oversampling in kernel space of support vector machines. IEEE Trans. Neural Networks Learn. Syst., 2017, 29(9), 4065–4076.

  • Devikanniga, D., Ramu, A. and Haldorai, A., Efficient diagnosis of liver disease using support vector machine optimized with crows search algorithm. EAI Endorsed Trans. Energy Web, 2020, 7(29), 1–10.

  • Sarica, A., Cerasa, A. and Quattrone, A., Random forest algorithm for the classification of neuroimaging data in Alzheimer’s disease: a systematic review. Front. Aging Neurosci., 2017, 9, 329–340.

  • Lakshmanaprabu, S. K., Shankar, K., Ilayaraja, M., Nasir, A. W., Vijayakumar, V. and Chilamkurti, N., Random forest for big data classification in the internet of things using optimal features. Int. J. Mach. Learn. Cybern., 2019, 10(10), 2609–2618.

  • Demidova, L. A., Klyueva, I. A. and Pylkin, A. N., Hybrid approach to improving the results of the SVM classification using the random forest algorithm. Proc. Comput. Sci., 2019, 150, 455–461.


Abstract Views: 245

PDF Views: 135




  • Performance of Advanced Machine Learning Models in the Prediction of Amylose Content in Rice Using Internet of Things-Based Colorimetric Sensor

Abstract Views: 245  |  PDF Views: 135

Authors

Shrinivas Deshpande
ICAR-Krishi Vigyan Kendra, Kandali, Hassan 573 217, India
Udaykumar Nidoni
Department of Processing and Food Engineering, College of Agricultural Engineering, University of Agricultural Sciences, Raichur 584 104, India
Sharanagouda Hiregoudar
Department of Processing and Food Engineering, College of Agricultural Engineering, University of Agricultural Sciences, Raichur 584 104, India
K. T. Ramappa
Department of Processing and Food Engineering, College of Agricultural Engineering, University of Agricultural Sciences, Raichur 584 104, India
Devanand Maski
Department of Renewable Energy Engineering, College of Agricultural Engineering, University of Agricultural Sciences, Raichur 584 104, India
Nagaraj Naik
Pesticide Residue and Food Quality Analysis Laboratory, College of Agricultural Engineering, University of Agricultural Sciences, Raichur 584 104, India

Abstract


Rice ageing is a complicated process that is difficult to examine methodically. Several physicochemical properties of rice change with age as a function of moisture content and storage temperature. Among these qualities, amylose content is the most important and numerous metrics depend on it. Several sensors, Internet of Things, Information and Communication Technology, artificial intelligence and machine learning (ML) approaches are being used in technological interventions to tackle this problem. In the present study, seven advanced ML models were evaluated to classify the different concentrations of amylose using light-intensity data obtained by the novel colorimetric amylose sensor. From the performance of the evaluated ML models, it was observed that for the light intensity dataset obtained from the sensor, higher and similar model parameters and an accuracy value of 0.77 were observed for both artificial neural network (ANN) and k-nearest neighbour (KNN) algorithms, followed by accuracy values of 0.75, 0.74, 0.65, 0.61 and 0.61 respectively, for the decision tree, random forest, AdaBoost, logistic regression and support vector machine algorithms. Thus ANN and KNN are promising in predicting the different classes of amylose in rice.

Keywords


Amylose Content, Artificial Intelligence, Machine Learning, Mathematical Modelling, Rice.

References





DOI: https://doi.org/10.18520/cs%2Fv124%2Fi6%2F722-730