Proactive Crop Supervision with Machine Learning Algorithms for Yield Improvement

  IJCTT-book-cover
 
         
 
© 2020 by IJCTT Journal
Volume-68 Issue-4
Year of Publication : 2020
Authors : Kusum Lata , Sajidullah S. Khan
DOI :  10.14445/22312803/IJCTT-V68I4P104

How to Cite?

Kusum Lata , Sajidullah S. Khan, "Proactive Crop Supervision with Machine Learning Algorithms for Yield Improvement," International Journal of Computer Trends and Technology, vol. 68, no. 4, pp. 14-20, 2020. Crossref, https://doi.org/10.14445/22312803/IJCTT-V68I4P104

Abstract
Machine learning is the revolutionary approach to solve the complex task in order to obtain the optimal preferred results. In the internet age large amount data is available to analyze and transform it to useful information. This analysis of this data is possible by applying machine leaning algorithms to create the relations between different data volumes. Here in this paper we will discuss the available machine leaning algorithms which can be implemented to improve the crop yield prediction with the help of agricultural data sets. This will enable the farmers and governments to get the preferred output which will further boost the Indian economy.

Keywords
Machine learning, Crop, SVM, Classification and regression.

Reference
[1] J. Liu, C. E. Goering, Lei Tian, 2001. “A neural network for setting target corn yields”. Transactions of the American Society of Agricultural Engineers44 (3):705-713.
[2] SamuelA.L,” Some Studies in Machine Learning Using the Game of Checkers ”. IBM J.Res. Dev. 1959, 44,206 226.
[3] Marcello Donatelli, Amit Kumar Srivastava, Gregory Duveiller, Stefan Niemeyer and Davide Fumagalli,” Climate change impact and potential adaptation strategies under alternate realizations of climate scenarios for three major crops in Europe ”, Environmental Research Letters, vol. 10, no. 7, Jul 2015, Art. No. 075005.
[4] Rakesh Kumar, M.P. Singh, Prabhat Kumar, J.P. Singh, Crop Se lection Method to maximize crop yield rate using machine learning technique ”,2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM),27 August 2015
[5] Report on Economic Survey of Maharashtra 2012 2013, Directorate of Economics and Statistics ””, Planning Department, Government of Maharashtra, Mumbai (2013).
[6] D. Diepeveen and L. Armstrong, “ Identifying key crop performance traits using data mining ” World Conference on Agriculture, Info rmation and IT, 2008.
[7] Alexander Murynin, Konstantin Gorokhovskiy and Vladimir Ignatie,“Efficiency of crop yield forecasting depending on the moment of prediction based on large remote sensing data set” retrieved fromhttp://worldcomp-proceedings.com/proc/p2013/DMI8036.pdf
[8] Hemageetha, N., “A survey on application of data mining techniques to analyze the soil for agricultural purpose”, 3rd International Conference on Computing for Sustainable Global Development (INDIACom), pp.3112-3117, 2016.
[9] Wu Fan, ChenChong, GuoXiaoling, Yu Hua, Wang Juyun. “Prediction of crop yield using big data”. 8th International Symposium on Computational Intelligence and Design (ISCID).2015; 1, 255-260.
[10] Monali Paul, Santosh K. Vishwakarma, Ashok Verma. Analysis of soil behavior and p rediction of crop yield using data mining approach ””. Computational Intelligence and Communication Networks (CICN). 2015; 766 771.
[11] Subhadra Mishra, Debahuti Mishra, GourHariSantra,” Applications of machine learning techniques in agricultural crop production: a review paper ””. Indian Journal of Science and Technology.2016, 9(38), 1 14
[12] Kushwaha, A.K., SwetaBhattachrya, “Crop yield prediction using Agro Algorithm in Hadoop”, International Journal of Computer Science and Information Technology & Security (IJCSITS), Vol. 5- No2, pp.271-274, 2015.
[13] Sujatha, R., Isakki, P., “A study on crop yield forecasting using classification techniques”, International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE), pp.1-4, 2016.
[14] N.Gandhi and L.J. Armstrong, “Applying data mining techniques to predict yield of rice in Humid Subtropical Climatic Zone of India”, Proceedings of the 10th INDIACom-2016, 3rd 2016 IEEE International Conference on Computing for Sustainable Global Development, New Delhi, India, 16th to 18th March 2016.
[15] N. Gandhi and L. Armstrong, “Rice Crop Yield forecasting of Tropical Wet and Dry climatic zone of India using data mining techniques”, IEEE International Conference on Advances in Computer Applications (ICACA), pp. 357-363, 2016.
[16] Shweta Srivastava, Diwakar Yagysen,”Implementaion of Genetic Algorithm for Agriculture System”, International Journal of New Innovations in Engineering and Technology Volume 5 Issue 1-May 2016.
[17] Shruti Mishra, Priyanka Paygude,Snehal Chaudhary, Sonali Idate, "Use of data mining in crop yield prediction",2018 2nd International Conference on Inventive Systems and Control (ICISC).
[18] Rossana MC, L. D. (2013). “A Prediction Model Framework for Crop Yield Prediction”. Asia Pacific Industrial Engineering and Management Society Conference Proceedings Cebu, Philippines, 185.
[19] R.Kalpana, N.Shanti and S.Arumugam, “A survey on data mining techniques in Agriculture”, International Journal of Advances in Computer Science and Technology, vol. 3, No. 8,426- 431, 2014.
[20] Dr Shirin Bhanu Koduri, Loshma Gunisetti, Ch Raja Ramesh, K V Mutyalu and D. Ganesh,” Prediction of crop production using AdaBoost regression Method”, International conference on computer vision and machine learning,Conf. Series 1228 (2019)