IJEP 42(1): 52-58 : Vol. 42 Issue. 1 (January 2022)
1. Kalasalingam Academy of Research and Education, Department of Computer Applications, Krishnankoil – 626 126, Tamil Nadu, India
2. Rajapalayam Rajus College, Department of Chemistry, Rajapalayam – 626 117, Tamil Nadu, India
Abstract
In India, there are most of the people get their incomes through agriculture. Plants require various mineral elements for their normal growth and development. The most important elements needed are C, H, O, N, P, K, Ca, Mn, Zn, Mo and Cl. These elements are very essential for plants’ growth. Currently there are lot of mobile applications, software and technologies available in agriculture to get rapid information. But the lack of awareness about these kinds of technologies the farmers suffer a lot and still they are applying traditional methods in agriculture. Nowadays use of natural and chemical fertilizers on crops are the important issues in agriculture. The farmers find it hard to identify the deficiencies in the soil, pH value, EC, soil type and soil texture, choose the correct crops to increase the production. This paper, uses random forest classification algorithm to identify the soil fertility and crop selection in Tirunelveli district. Compared to existing methods, the proposed experimental results show that the random forest classification algorithm for agricultural data analysis produces high accuracy and less processing time.
Keywords
Attribute selection, Agriculture, Essential nutrient, Soil fertility, Random forest method
References
- Navarro, H.H., et al. 2016. A decision support system for managing irrigation in agriculture. Computers Electronics Agric., 124: 121-131.
- Antonopoulou, E., et al. 2010. Web and mobile technologies in a prototype DSS for major field crops. Computers Electronics Agric., 70: 292-301.
- Bhargavi, P. and S. Jyothi. 2011. Soil classification using data mining techniques: A comparative study. Int. J. Eng. Trends Tech., 2: 55-58.
- Veenadhari, S., B. Misra and C.D. Singh. 2011. Data mining techniques for predicting crop productivity – A review article. Int. J. Computer Sci. Tech., 2(1): 98-100.
- Vineya, P. and A. Valarmathi. 2016. Agriculture analysis for next generation high tech farming in data mining. Int. J. Adv. Res. Computer Sci. Software Eng., 6(5): 481-488.
- Available at: www.ikisan.com.
- Manjula, E. and S. Djodiltachoumy. 2017. A model for prediction of crop yield. Int. J. Computational Intelligence Informatics. 6(4): 298-305.
- Ghosh, S. and S. Koley. Machine learning for soil fertility and plant nutrient management using back propagation neural networks. Int. J. Recent Innovation Trends Computing Communication. 2(2): 292-297.
- Schönfeld, M., R. Heil and L. Bittner. 2018. Big data on a farm: Smart farming. In Big data in context. pp 109-120. DOI: 10.1007/978-3-31962461-7_12.
- Han, J. and M. Kamber. 2012. Data mining: Concepts and techniques (3rd edn). Morgan Kaufmann Publishers, San Francisco.
- Archana, S. and K. Elangovan. 2013. Survey of classification techniques in data mining. Int. J. Computer Sci. Mobile Applications. 2(2): 65-71.
- Oad, R., et al. 2009. Decision support systems for efficient irrigation in the middle Rio Grande Valley. ASCE J. Irrigation Drainage. 135(2): 177-185.
- Perini, A. and A. Susi. 2004. Developing a decision support system for integrated production in agriculture. Env. Modelling Software. 19(9): 821-829.
- Karthigadevi, K., S. Balamurali and M. Venkatesulu. 2018. Wormhole attack detection and prevention using EIGRP protocol based on round trip time. J. Cyber Security Mobility. 7(1-2): 215-218.
- Thorp, K.R., et al. 2008. Methodology for the use of DSSAT models for precision agriculture decision support. Computers Electronics Agric., 64(2): 276-285.
- Karthigadevi, K., S. Balamurali and M. Venkatesulu. 2020. Based on neighbour density estimation technique to improve the quality of service and to detect and prevent the sinkhole attack in wireless sensor network in 2019. International Conference on Intelligent techniques in control, optimization and signal processing (INCOS). IEEE Proceedings.
- Bachu, V., K. Polepalli and G.S. Reddy. 2006. Esagu: An IT based personalized agricultural extension system prototype – Analysis of 51 farmers’ case studies. Int. J. Edu. Develop. ICT. 2(1): 79-94.
- Karthigadevi, K., S. Balamurali and M. Venkatesulu. 2018. Watchdog- Round trip time method to detect and prevent the wormhole attack using AODV routing protocol. J. Web Eng., 17(6): 3619-3628.
- Nigam, A., P. Kabra and P. Doke. 2011. Augmented reality in agriculture. 7th International Conference on Wireless and mobile computing, networking and communications (WiMob). IEEE Proceedings, pp 445-448.
- Karthigadevi, K., S. Balamurali and M. Venkatesulu. 2017. Improving quality of service in wireless sensor networks using neighbour constraint transmission centric distributed sinkhole detection and network simulator 2. ARPN J. Eng. Appl. Sci., 12(4): 1197-1201.
- Wang, J., et al. 2013. Online group feature selection. 23rd International Joint Conference on Artificial intelligence (IJCAI 2013), Beijing, China. Proceedings, pp 1757-1763.
