An Empirical Evaluation of Machine Learning Techniques for Crop Prediction.

G. Mariammal; A. Suruliandi; S. P. Raja; E. Poongothai

doi:10.9781/ijimai.2022.12.004

Authors

G. Mariammal Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology
A. Suruliandi Manonmaniam Sundaranar University
S. P. Raja Vellore Institute of Technology University
E. Poongothai SRM Institute of Science and Technology

DOI:

https://doi.org/10.9781/ijimai.2022.12.004

Keywords:

Classification, Environment, Character Identification, Machine Learning

Abstract

Agriculture is the primary source driving the economic growth of every country worldwide. Crop prediction, which is critical to agriculture, depends on the soil and environment. Nutrient levels differ from area to area and greatly influence in crop cultivation. Earlier, the tasks of crop forecast and cultivation were undertaken by farmers themselves. Today, however, crop prediction is determined by climatic variations. This is where machine learning algorithms step in to identify the most relevant crop for cultivation. This research undertakes an empirical analysis using the bagging, random forest, support vector machine, decision tree, Naïve Bayes and k-nearest neighbor classifiers to predict the most appropriate cultivable crop for certain areas, based on environment and soil traits. Further, the suitability of the classifiers is examined using a GitHub prisoners’ dataset. The experimental results of all the classification techniques were assessed to show that the ensemble outclassed the rest with respect to every performance metric.

Downloads

Download data is not yet available.

References

S. A. Z. Rahman, K. Chandra Mitra and S. M. Mohidul Islam, “Soil Classification Using Machine Learning Methods and Crop Suggestion Based on Soil Series,” in 2018 21st International Conference of Computer and Information Technology (ICCIT), Dhaka, Bangladesh, 2018, pp.1-4, doi: https://doi.org/10.1109/ICCITECHN.2018.8631943

William A. Belson, “Matching and prediction on the principle of biological classification,” in Journal of the Royal Statistical Society: Series C (Applied Statistics) vol. 8, pp. 65-75, 1959, doi: https://doi.org/10.2307/2985543

Richard O. Duda, Peter E. Hart, and David G. Stork. “Pattern classification and scene analysis”, in New York: Wiley, vol. 3, 1973, doi: https://doi.org/10.1086/620282

Gordon V. Kass, “An exploratory technique for investigating large quantities of categorical data”, in Journal of the Royal Statistical Society: Series C (Applied Statistics), vol. 29, pp. 119-127, 1980, doi: https://doi.org/10.2307/2986296

Leo Breiman, Jerome Friedman, Charles J. Stone, and Richard A. Olshen, “Classification and regression trees”, in CRC press, 1984, doi: https://doi.org/10.1201/9781315139470

R.E. Neapolitan, “Models for reasoning under uncertainty”, in Appl. Artif. Intell. vol. 1, pp. 337–366, 1987, doi: https://doi.org/10.1080/08839518708927979

T. Kohonen, “Learning vector quantization”, in Neural Netw, vol. 1, pp. 303, 1988, doi: https://doi.org/10.1007/978-3-642-97610-0_6

Christopher G. Atkeson, Andrew W. Moore, and Stefan Schaal, “Locally weighted learning”, In Lazy learning, Springer, Dordrecht, 1997, pp. 11- 73, doi: https://doi.org/10.1023/A:1006559212014

J. Pearl, “Probabilistic Reasoning in Intelligent Systems”, in Morgan Kauffmann San Mateo, vol. 88, pp. 552, 1988, doi: https://doi.org/10.1016/C2009-0-27609-4

J.R Quinlan, “C4.5: Programs for Machine Learning”, in Morgan Kaufmann Publishers Inc.: San Francisco, CA, USA, vol. 1, 1992, doi: https://doi.org/10.1007/BF00993309

S.J. Russell, and P Norvig, “Artificial Intelligence: A Modern Approach”, in Prentice Hall: Upper Saddle River, NJ, USA, vol. 9, 1995, doi: 10.1016/j.artint.2011.01.005.

L. Breiman, “Bagging Predictors”, in Mach. Learn, vol. 24, pp. 123–140, 1996, doi: https://doi.org/10.1007/BF00058655

Yoav Freund, and Robert E. Schapire, “Experiments with a new boosting algorithm”, In ICML 96, pp. 148-156, 1996, doi: https://dl.acm.org/doi/10.5555/3091696.3091715

Robert E. Schapire, “A brief introduction to boosting”, In IJCAI 99, pp. 1401-1406, 1999, doi: https://dl.acm.org/doi/10.5555/1624312.1624417

A. Smola, “Regression Estimation with Support Vector Learning Machines”, in Master’s Thesis, The Technical University of Munich, Munich, Germany, pp. 1–78, 1996.

Johan A.K. Suykens, and Joos Vandewalle, “Least squares support vector machine classifiers”, in Neural processing letters, vol. 9, pp. 293-300, 1999, doi: https://doi.org/10.1023/A:1018628609742

Leo Breiman, “Random forests”, in Machine learning, vol. 45, pp. 5-32, 2001, doi: https://doi.org/10.1023/A:1010933404324

J.A.K. Suykens, Van Gestel, T, De Brabanter, J, De Moor, B and J Vandewalle, “Least Squares Support Vector Machines”, in World Scientific: Singapore, 2002, doi: https://doi.org/10.1142/5089

Galvao, Roberto Kawakami Harrop, Mario Cesar Ugulino Araujo, Wallace Duarte Fragoso, Edvan Cirino Silva, Gledson Emidio Jose, Sofacles Figueredo Carreiro Soares, and Henrique Mohallem Paiva, “A variable elimination method to improve the parsimony of MLR models using the successive projections algorithm”, in Chemometrics and intelligent laboratory systems, vol. 92, pp. 83-91, 2008, doi: https://doi.org/10.1016/j.chemolab.2007.12.004

M.S.P. Babu, N.V. Ramana Murty and S.V.N.L Narayana, “A web based tomato crop expert information system based on artificial intelligence and machine learning algorithms”, in International Journal of Computer Science and Information Technologies, vol. 1, pp. 1–5, 2010, doi: 10.1.1.206.2072

S. Veenadhari, Bharat Misra, and C. D. Singh, “Machine learning approach for forecasting crop yield based on climatic parameters”, In 2014 International Conference on Computer Communication and Informatics, IEEE, pp. 1-5, 2014, doi: https://doi.org/10.1109/ICCCI.2014.6921718

Paul Monali, Santosh K. Vishwakarma, and Ashok Verma, “Analysis of soil behaviour and prediction of crop yield using data mining approach”, In 2015 International Conference on Computational Intelligence and Communication Networks (CICN), IEEE, pp. 766-771, 2015, doi: https://doi.org/10.1109/CICN.2015.156

Jig Han Jeong, Jonathan P. Resop, Nathaniel D. Mueller, David H. Fleisher, Kyungdahm Yun, Ethan E. Butler, Dennis J. Timlin et al. “Random forests for global and regional crop yield predictions”, in PLoS One, vol. 11, 2016, doi: https://doi.org/10.1371/journal.pone.0156571

V. Sellam, and E. Poovammal, “Prediction of crop yield using regression analysis”, in Indian Journal of Science and Technology, vol. 9, pp. 5, 2016, doi: 10.17485/ijst/2016/v9i38/91714.

S. Pudumalar, E. Ramanujam, R. Harine Rajashree, C. Kavya, T. Kiruthika, and J. Nisha, “Crop recommendation system for precision agriculture”, In 2016 Eighth International Conference on Advanced Computing (ICoAC), IEEE, pp. 32-36, 2017, doi: https://doi.org/10.1109/ICoAC.2017.7951740

Dipika H Zala, “Review on use of BAGGING technique in agriculture crop yield prediction”, in International Journal for Scientific Research & Development, vol. 6, 2018.

Fabrizio Balducci, Donato Impedovo, and Giuseppe Pirlo, “Machine learning applications on agricultural datasets for smart farm enhancement”, in Machines, vol. 6, pp. 38, 2018, doi: https://doi.org/10.3390/machines6030038

Jahan Raunak, “Applying Naive Bayes Classification Technique for Classification of Improved Agricultural Land soils”, in International Journal for Research in Applied Science & Engineering Technology (IJRASET), vol. 6, pp. 189-193, 2018, doi: https://10.22214/ijraset.2018.5030

P. Priya, U. Muthaiah, and M. Balamurugan, “Predicting yield of the crop using machine learning algorithm”, in International Journal of Engineering Sciences & Research Technology, vol. 7, pp. 1-7, 2018, doi: 10.5281/zenodo.1212821.

A. Suresh, P. Ganesh Kumar, and M. Ramalatha, “Prediction of major crop yields of Tamilnadu using K-means and Modified KNN”, In 2018 3rd International Conference on Communication and Electronics Systems (ICCES), IEEE, pp. 88-93, 2018, doi: 10.1109/CESYS.2018.8723956.

D. Anantha Reddy, Bhagyashri Dadore, and Aarti Watekar, “Crop Recommendation System to Maximize Crop Yield in Ramtek region using Machine Learning”, in International Journal of Scientific Research in Science and Technology, vol. 6, pp. 485 - 489, 2019, doi: https://doi.org/10.32628/IJSRST196172

Ivan Kholod, Andrey Shorov, and Sergei Gorlatch, “Improving Parallel Data Mining for Different Data Distributions in IoT Systems”, In International Symposium on Intelligent and Distributed Computing, Springer, Cham, pp. 75-85, 2019, doi: https://doi.org/10.1007/978-3-030-32258-8_9

Grant Anderson, “Random relational rules”, in PhD diss., The University of Waikato, 2008.

Angelina Tzacheva, Jaishree Ranganathan, and Sai Yesawy Mylavarapu, “Actionable Pattern Discovery for Tweet Emotions”, In International Conference on Applied Human Factors and Ergonomics, Springer, Cham, pp. 46-57, 2019, doi: 10.1007/978-3-030-20454-9_5.

Mateusz Lango, and Jerzy Stefanowski, “Multi-class and feature selection extensions of roughly balanced bagging for imbalanced data”, in Journal of Intelligent Information Systems, vol. 50, pp. 97-127, 2018, doi: https://doi.org/10.1007/s10844-017-0446-7

H. Benjamin Fredrick David, A. Suruliandi, and S.P. Raja, “Preventing crimes ahead of time by predicting crime propensity in released prisoners using Data Mining techniques”, in International Journal of Applied Decision Sciences, vol. 12, pp. 307–336, 2019, doi: 10.1504/IJADS.2019.100433.

Dua, D. and Graff, C. UCI Machine Learning Repository [http://archive.ics.uci.edu/ml], Irvine, CA: University of California, School of Information and Computer Science, 2019.

Mariammal, G., A. Suruliandi, S. P. Raja, and E. Poongothai. “Prediction of Land Suitability for Crop Cultivation Based on Soil and Environmental Characteristics Using Modified Recursive Feature Elimination Technique With Various Classifiers.” in IEEE Transactions on Computational Social Systems, vol. 8, no. 5, 2021, pp. 1132-1142, doi: 10.1109/TCSS.2021.3074534.

Ganesan, Mariammal, Suruliandi Andavar, and Raja Soosaimarian Peter Raj. “Prediction of Land Suitability for Crop Cultivation Using Classification Techniques.” in Brazilian Archives of Biology and Technology, vol. 64, 2021.

Bhaik, A., Singh, V., Gandotra, E., & Gupta, D. (InPress). Detection of Improperly Worn Face Masks using Deep Learning – A Preventive Measure Against the Spread of COVID-19. International Journal of Interactive Multimedia and Artificial Intelligence, In Press (In Press), 1-12. http://doi.org/10.9781/ijimai.2021.09.003

Alvarez, P., García de Quirós, J., & Baldassarri, S. (InPress). RIADA: A Machine-Learning Based Infrastructure for Recognising the Emotions of Spotify Songs. International Journal of Interactive Multimedia and Artificial Intelligence, In Press (In Press), 1-14. http://doi.org/10.9781/ijimai.2022.04.002

Sánchez-Torres, F., González, I., & Dobrescu, C. C. (2022). Machine Learning in Business Intelligence 4.0: Cost Control in a Destination Hotel. International Journal of Interactive Multimedia and Artificial Intelligence, 7 (Special Issue on Artificial Intelligence in Economics, Finance and Business), 86-95. http://doi.org/10.9781/ijimai.2022.02.008