Abstract
In relation to the evaluation of the soil breeding status of a region or realm, the soil characteristics are an important aspect in terms of agricultural production. Nitrogen, phosphorus, potassium, and sulfur are important elements of soil that regulate its fertility and yield of crops. Due to the low efficiency of other inputs or due to the use of unbalanced and inadequate fertilizer, the reaction of chemical fertilizer nutrients (production) efficiency in recent years has reduced considerably under intensive agriculture. Stability in crop productivity cannot be extended without the judicial use of macro and micro nutrients to overcome existing deficiencies. The information on the availability of macro nutrients in the study area is low. Therefore, the current study has been done to know the condition of soil nutrients. Use of advanced agricultural technology can help in predicting soil nutrient content and can help farmers to decide the amount of fertilizers to use on a particular land. The proposed study focuses on the accurate prediction of N-P-K content in the given land by utilizing the predication method using Adaboost.RT method. A comparison is also made in between the nutrient utilized using traditional methods and the proposed method. Experimental results show that the proposed stream outperforms with other existing methodologies.
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References
Lundström, C., Lindblom, J.: Considering farmers’ situated knowledge of using agricultural decision support systems (AgriDSS) to Foster farming practices: the case of CropSAT. Agric. Syst. 159, 9–20 (2018)
Arunima, G., Antaryam, M.: Evaluation of STCR targeted yield approach on pumpkin (Cucurbita moschata) under rice-pumpkin cropping system. Int. J. Tropic. Agric. 33(2 (Part IV)), 1583–1586 (2015)
Agriculture in India - of Planning Commission. http://www.planningcommission.nic.in/reports/sereport/ser/vision2025/agricul.doc
STCR Crop Wise Recommendations. http://www.iiss.nic.in/downloads/stcrCropwiseRecommendations
Rao, N.H.: Big Data and Climate Smart Agriculture-Review of Current Status and Implications for Agricultural Research and Innovation in India (2017)
De Mauro, A., Greco, M., Grimaldi, M.: A formal definition of Big Data based on its essential features. Libr. Rev. 65(3), 122–135 (2016)
Friess, P.: Digitising the Industry-Internet of Things Connecting the Physical, Digital and Virtual Worlds. River Publishers (2016)
Wolfert, S., Ge, L., Verdouw, C., Bogaardt, M.J.: Big Data in smart farming–a review. Agric. Syst. 153, 69–80 (2017)
Morshed, A., Dutta, R., Aryal, J.: Recommending environmental knowledge as linked open data cloud using semantic machine learning. In: 2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW), pp. 27–28. IEEE (2013)
Parent, B., Tardieu, F.: Can current crop models be used in the phenotyping era for predicting the genetic variability of yield of plants subjected to drought or high temperature? J. Exp. Bot. 65(21), 6179–6189 (2014)
Rosenzweig, C.E., Antle, J., Elliott, J.: Assessing impacts of climate change on food security worldwide (2015)
Mishra, S., Mishra, D., Santra, G.H.: Applications of machine learning techniques in agricultural crop production: a review paper. Indian J. Sci. Technol. 9(38) (2016)
Khoshnevisan, B., Rafiee, S., Omid, M., Mousazadeh, H., Rajaeifar, M.A.: Application of artificial neural networks for prediction of output energy and GHG emissions in potato production in Iran. Agric. Syst. 123, 120–127 (2014)
Patil, A., Beldar, M., Naik, A., Deshpande, S.: Smart farming using Arduino and data mining. In: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), pp. 1913–1917. IEEE (2016)
Bendre, M.R., Thool, R.C., Thool, V.R.: Big data in precision agriculture: Weather forecasting for future farming. In: 2015 1st International Conference on Next Generation Computing Technologies (NGCT), pp. 744–750. IEEE, September 2015
Hot, E., Popović-Bugarin, V.: Soil data clustering by using K-means and fuzzy K-means algorithm. In: 2015 23rd Telecommunications Forum Telfor (TELFOR), pp. 890–893. IEEE, November 2015
Navarro-HellÃn, H., MartÃnez-del-Rincon, J., Domingo-Miguel, R., Soto-Valles, F., Torres-Sánchez, R.: A decision support system for managing irrigation in agriculture. Comput. Electron. Agric. 124, 121–131 (2016)
Villarrubia, G., Paz, J.F.D., Iglesia, D.H., Bajo, J.: Combining multi-agent systems and wireless sensor networks for monitoring crop irrigation. Sensors 17(8), 1775 (2017)
Shrestha, D.L., Solomatine, D.P.: Experiments with AdaBoost.RT, an improved boosting scheme for regression. Neural Comput. 18(7), 1678–1710 (2006)
Acknowledgments
This work was supported by the Science and Research Board (SERB), Govt. of India with the grant number ECR/2017/001273. The authors also wish to express their gratitude and heartiest thanks to the Department of Computer Science & Engineering, Indian Institute of Technology (ISM), Dhanbad, India for providing their research support.
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Priya, R., Ramesh, D. (2018). Adaboost.RT Based Soil N-P-K Prediction Model for Soil and Crop Specific Data: A Predictive Modelling Approach. In: Mondal, A., Gupta, H., Srivastava, J., Reddy, P., Somayajulu, D. (eds) Big Data Analytics. BDA 2018. Lecture Notes in Computer Science(), vol 11297. Springer, Cham. https://doi.org/10.1007/978-3-030-04780-1_22
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