R. Mythili,AdityaVenkatakrishnan,T. Srinivasan,P. YashwanthSai Kumar,




Crop yield prediction,Support Vector Machine,Least Squares Support Vector machine,Data Analytics,Agriculture,


Predominantly in India, Agriculture is the most significant income generating segments and also a wellspring of endurance. Various occasional, financial and natural incidents impact the yield creation, yet erratic changes in these cases lead to an incredible misfortune for the Farmers. These dangers are to be decreased by utilizing reasonable mining methodologies on the identified data of soil type, temperature, environmental weights, mugginess and yield type. While, harvest and climate gauging can be anticipated by getting valuable bits of knowledge from this agricultural information that guides the Farmers to choose the yield, meanwhile they may need to plant for the expected year prompting extreme benefits. This paper presents an overview of different calculations utilized for climate, crop yield, and harvest forecast of the proposed crop yield prediction method using Least Squares Support Vector Machine (LS-SVM).


I. A.Na, W. Isaac, S. Varshney and E. Khan, “An IoT based system for remote monitoring of soil characteristics,” 2016 International Conference on Information Technology (InCITe) – The Next Generation IT Summit on the Theme – Internet of Things: Connect your Worlds, Noida, 2016, pp.316-320, doi: 10.1109/INCITE.2016.7857638.
II. AnupamaMahato, “Climate Change and its Impact on Agriculture”, International Journal of Scientific and Research Publications, Volume 4, Issue 4, April 2014.
III. Awanit Kumar, Shiv Kumar, “Prediction of production of crops using K-Means and Fuzzy Logic”, International Journal of Computer Science and Mobile Computing, Vol.4 Issue.8, August- 2015, pg. 44-56.
IV. Birthal, P.S., Kumar, S., Negi, D.S. and Roy, D. (2015), “The impacts of information on returns from farming: evidence from a nationally representative farm survey in India. Agricultural Economics”, 46: 549-561. doi:10.1111/agec.12181
V. Dhivya B, Manjula, Siva Bharathi, Madhumathi, “A Survey on Crop Yield Prediction based on Agricultural Data”, International Journal of Innovative Research in Science, Engineering and Technology, Vol. 6, Issue 3, March 2017.
VI. G. Ravichandran and R. S. Koteeshwari, “Agricultural crop predictor and advisor using ANN for smartphones,” 2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS), Pudukkottai, 2016, pp. 1-6. doi: 10.1109/ICETETS.2016.7603053.
VII. https://en.wikipedia.org/wiki/Linear_regression.
VIII. https://en.wikipedia.org/wiki/Nonlinear_regression.
IX. Iooss, Bertrand &Lemaître, Paul.(2014). A Review on Global Sensitivity Analysis Methods.Operations Research/ Computer Science Interfaces Series. 59. 10.1007/978-1-4899-7547-8-5.
X. JapneetKaur,” Impact of Climate Change on Agricultural Productivity and Food Security Resulting in Poverty in India”, Final Thesis, Master’s Degree Programme – Second Cycle, UniversitaCaFoscariVenezia, 2017.
XI. J. Shenoy and Y. Pingle, “IOT in agriculture”, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, 2016, pp. 1456-1458.

XII. L. Leroux, C. Baron, B. Zoungrana, S. B. Traoré, D. Lo Seen and A. Bégué, “Crop Monitoring Using Vegetation and Thermal Indices for Yield Estimates: Case Study of a Rainfed Cereal in Semi-Arid West Africa,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 1, pp. 347-362, Jan. 2016. doi: 10.1109/JSTARS.2015.2501343.
XIII. M. R. Bendre, R. C. Thool and V. R. Thool, “Big data in precision agriculture: Weather forecasting for future farming,” 2015 1st International Conference on Next Generation Computing Technologies (NGCT), Dehradun, 2015, pp. 744-750. doi: 10.1109/NGCT.2015.7375220.
XIV. M. Paul, S. K. Vishwakarma and A. Verma, “Analysis of Soil Behaviour and Prediction of Crop Yield Using Data Mining Approach,” 2015 International Conference on Computational Intelligence and Communication Networks (CICN), Jabalpur, 2015, pp. 766-771. doi: 10.1109/CICN.2015.156.
XV. N. Hemageetha, “A survey on application of data mining techniques to analyze the soil for agricultural purpose,” 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, 2016, pp. 3112-3117.
XVI. R. Mythili, MeenakshiKumari, ApoorvTripathi, Neha Pal, “IoT Based Smart Farm Monitoring System”, International Journal of Recent Technology and Engineering, ISSN: 2277-3878, Volume-8 Issue-4, November 2019.
XVII. S. Nagini, T. V. R. Kanth and B. V. Kiranmayee, “Agriculture yield prediction using predictive analytic techniques,” 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), Noida, 2016, pp. 783-788. doi: 10.1109/IC3I.2016.7918789.
XVIII. Soybeandataset, “https://archive.ics.uci.edu/ml/datasets.php?format=mat&task=cla&att=&area=life&numAtt=10to100&numIns=&type=mvar&sort=dateUp&view=list”.
XIX. Zhihua Zhang, Multivariate Time Series Analysis in Climate and Environmental Research, 2018, Springer Nature Switzerland.

View Download