Abstract
Big Data deals with huge volume of growing datasets, which are complex and having various autonomous sources. Storing and processing of such huge data was very tedious using earlier technologies so thus, the concept of Big Data came into existence. It was a tedious task for the end users to correctly identify the required data from a huge volume of unstructured data. The process of converting unstructured data into an organized and structured form, which will help the end user to easily retrieve the required data, is performed. So, applying classification techniques upon huge transactional database will provide required data to the end users from large datasets in a more simple way via Internet of Things (IoT). The two main categories of classification techniques are supervised and unsupervised techniques. In this paper, we have analyzed the performance of various supervised machine learning techniques on agricultural big dataset. Further, this paper shows the application of various analyzed techniques, its advantages, and limitations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
S.K. Lakshmanaprabu, K. Shankar, A. Khanna, D. Gupta, J.J. Rodrigues, P.R. Pinheiro, V.H.C. De Albuquerque, Effective features to classify big data using social Internet of Things. IEEE Access 6, 24196–24204 (2018)
N.G. Yethiraj, Applying data mining techniques in the field of agriculture and allied sciences. Int. J. Bus. Intell. 01(02) (2012, December). ISSN: 2278-2400
S.D. Sawaitul, K.P. Wagh, P.N. Chatur, Classification and prediction of future weather by using back propagation algorithm—an approach. Int. J. Emerg. Technol. Adv. Eng. 2(1), 110–113 (2012, January)
S.K. Lakshmanaprabu, K. Shankar, D. Gupta, A. Khanna, J.J.P.C. Rodrigues, P.R. Pinheiro, V.H.C. de Albuquerque, Ranking analysis for online customer reviews of products using opinion mining with clustering. Complexity, 2018, Article ID 3569351, 9 (2018). https://doi.org/10.1155/2018/3569351
K. Shankar, S.K. Lakshmanaprabu, D. Gupta, A. Maseleno, V.H.C. de Albuquerque, Optimal feature-based multi-kernel SVM approach for thyroid disease classification. J. Supercomput. (2018). https://doi.org/10.1007/s11227-018-2469-4
M. Muslihudin, R. Wanti, N. Hardono, K. Shankar, M. Ilayaraja, A. Maseleno, D.R.M. Fauzi, M. Masrur, S. Mukodimah, Prediction of layer chicken disease using fuzzy analytical hierarcy process. Int. J. Eng. Technol. 7(2.26), 90–94 (2018, June)
K. Shankar, Prediction of most risk factors in hepatitis disease using apriori algorithm. Res. J. Pharm. Biol. Chem. Sci. 8(5), 477–484 (2017)
R.V.Q. Srikant, R. Agrawal, Mining association rules with item constraints, in KDD, vol. 97 (1997, August), pp. 67–73
K. Karthikeyan, R. Sunder, K. Shankar, S.K. Lakshmanaprabu, V. Vijayakumar, M. Elhoseny, G. Manogaran, Energy consumption analysis of virtual machine migration in cloud using hybrid swarm optimization (ABC–BA). J. Supercomput. (2018). https://doi.org/10.1007/s11227-018-2583-3
A.E. Hassanien, R.M. Rizk-Allah, M. Elhoseny, A hybrid crow search algorithm based on rough searching scheme for solving engineering optimization problems. J. Ambient. Intell. Hum. Ized Comput. (2018). https://doi.org/10.1007/s12652-018-0924-y
M. Elhoseny, A. Hosny, A.E. Hassanien, K. Muhammad, A.K. Sangaiah, Secure automated forensic investigation for sustainable critical infrastructures compliant with green computing requirements. IEEE Trans. Sustain. Comput. PP(99) (2017). https://doi.org/10.1109/tsusc.2017.2782737
M. Sajjad, M. Nasir, K. Muhammad, S. Khan, Z. Jan, A.K. Sangaiah, M. Elhoseny, S.W. Baik, Raspberry Pi assisted face recognition framework for enhanced law-enforcement services in smart cities, in Future Generation Computer Systems (Elsevier, 2018). https://doi.org/10.1016/j.future.2017.11.013
M.J. Zaki, Parallel and distributed association mining: a survey. IEEE Concurr. 7(4), 14–25 (1999)
D. Ramesh, B. Vishnu Vardhan, Data mining techniques and applications to agricultural yield data. IJARCCE 2(9), (2013, September)
I. Jagielska, C. Mattehews, T. Whitfort, An investigation into the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acquisition for classification problems. Neurocomputing 24, 37–54 (1999)
K. Verheyen, D. Adriaens, M. Hermy, S. Deckers, High resolution continuous soil classification using morphological soil profile descriptions. Geoderma 101, 31–48 (2001)
S. Veenadhari, Crop productivity mapping based on decision tree and Bayesian classification. Unpublished M.Tech Thesis submitted to Makhanlal Chaturvedi National University of Journalism and Communication, Bhopal, 2007
A. Chinchulunn, P. Xanthopoulos, V. Tomaino, P.M. Pardalos, Data mining techniques in agricultural and environmental sciences, Int. J. Agric. Environ. Inf. Syst. 1(1), 26–40 (2010, January–June)
S. Veenadhari, B. Misra, C.D. Singh, Data mining techniques for predicting crop productivity—a review article. Int. J. Comput. Sci. Technol. (IJCST) 2(1) (2011, March)
D. Shalvi, N. De Claris, Unsupervised neural network approach to medical data mining techniques, in Proceedings of IEEE International Joint Conference on Neural Networks, Alaska (1998, May), pp. 171–176
B. Rajagopalan, U. Lal, A K-nearest neighbor simulator for daily precipitation and other weather variable. Water Resour. 35, 3089–3101 (1999)
A. Tellaeche, X.P. BurgosArtizzu, G. Pajares, A. Ribeiro, A vision-based hybrid classifier for weeds detection in precision agriculture through the Bayesian and Fuzzy k-Means paradigms, in Innovations in Hybrid Intelligent Systems (Springer, Berlin, Heidelberg, 2007), pp. 72–79
K. Somvanshi et al., Modeling and prediction of rainfall using artificial neural network and arima techniques. J. Ind. Geophys. Union 10(2), 141–151 (2006)
A. Mucherino, P. Papajorgji, P. Pardalos, Data Mining in Agriculture, vol. 34 (Springer, 2009)
A. Urtubia, J.R. Pérez-Correa, A. Soto, P. Pszczolkowski, Using data mining techniques to predict industrial wine problem fermentations. Food Control 18(12), 1512–1517 (2007)
X. Wu, X. Zhu, G.-Q. Wu, W. Ding, Data mining with Big Data. Trans. Knowl. Data Eng., 26(1) (2014, January). 1041–4347/14, IEEE
S. Beniwal, J. Arora, Classification and feature selection techniques in data mining. Int. J. Eng. Res. Technol. (IJERT) 1(6), 7 (2012). LiorRokach, OdedMaimon, “Clustering Methods”, Chap. 15
R. Xu, D. Wunsch, Survey of clustering algorithms. IEEE Trans. Neural Netw. 16(3), 645–678 (2005)
U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, From data mining to knowledge discovery in databases. AI Mag. 17(3), 37 (1996)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Anusuya, R., Krishnaveni, S. (2019). Performance Evaluation of Supervised Machine Learning Classifiers for Analyzing Agricultural Big Data. In: Elhoseny, M., Singh, A. (eds) Smart Network Inspired Paradigm and Approaches in IoT Applications. Springer, Singapore. https://doi.org/10.1007/978-981-13-8614-5_8
Download citation
DOI: https://doi.org/10.1007/978-981-13-8614-5_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-8613-8
Online ISBN: 978-981-13-8614-5
eBook Packages: Computer ScienceComputer Science (R0)