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  Prediction of Crop Yield Using Data Mining  
  Authors : Nishiba Kabeer; Dr.Loganathan.D; Cowsalya.T
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Agriculture is a major source of economy of the country. Agricultural crop production depends on various factors such as biology, climate, economy and geography. Several factors have different impacts on agriculture, which can be quantified using appropriate statistical methodologies .Predicting the crop yield well in advance prior to its harvest can help the farmers and Government organizations to make appropriate planning like storing, selling, fixing minimum support price, importing/exporting etc. As Prediction of crop deals with large set of database thus making this prediction system a perfect candidate for application of data mining. This work aims at finding suitable data models that achieve a high accuracy and a high generality in terms of yield prediction capabilities. The main aim is to create a user friendly interface for farmers, which gives the prediction of production using Data Mining techniques like Regression and Clustering based on available data in all districts of Kerala.


Published In : IJCSN Journal Volume 8, Issue 3

Date of Publication : June 2019

Pages : 300-304

Figures :04

Tables : --


Nishiba Kabeer : is currently pursuing M.E, CSE at SVS college of Engineering affiliated to Anna University. Her area of interests are Data Mining, Machine Learning, and Predictive Analysis.

Dr.D.Loganathan : is a Professor and Head of Computer Science and Engineering department in SVS College of Engineering, Coimbatore, Tamilnadu. He has published several research articles in various international journals and presented several research papers in various international and national conferences.

T.Cowsalya : working as a Assistant Professor in the Computer Science and Engineering department at SVS College of Engineering, Coimbatore, Tamilnadu.


Data mining, crop analysis, yield prediction, clustering, linear regression

The work demonstrated the potential use of data mining techniques in predicting the crop yield based on the input parameters average rainfall and area of field. The developed webpage is user friendly and the accuracy of predictions are above 90 per cent. The districts selected in the study indicating higher accuracy of prediction. The user friendly web page developed for predicting crop yield can be used by any user by providing average rainfall and area of that place. The process was adopted for all the districts of Kerala to improve and authenticate the validity of yield prediction which are useful for the farmers of Kerala for the prediction of a specific crop.


[1] D Ramesh, B Vishnu Vardhan. "Data Mining Techniques and Applications to Agricultural Yield Data". International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 9, September 2013. [2] S.Veenadhari, Dr. Bharat Misra, and Dr. CD Singh, "Machine learning approach for forecasting crop yield based on climatic parameters", International Conference on Computer Communication and Informatics (ICCCI 2014), Jan 2014, Coimbatore. [3] A.T.M Shakil Ahamed, Navid Tanzeem Mahmood, Nazmul Hossain, Mohammad Tanzir Kabir, Kallal Das, Faridur Rahman, and Rashedur M Rahman, "Applying Data Mining Techniques to Predict Annual Yield of Major Crops and Recommend Planting Different Crops in Different Districts in Bangladesh", IEEE 2015. [4] D Ramesh, and B Vishnu Vardhan, "Analysis of Crop Yield Prediction Using Data Mining Techniques", IJRET: International Journal of Research in Engineering and Technology, Jan 2015. [5] Ye Nong; Data Mining: Theories, Algorithms, and Examples, CRC Press, 2013. [6] http://books.irri.org/0471097608_content.pdf [7] http://docs.rapidminer.com/studio [8] http://www.barcapps.gov.bd/dbs/index.php [9] http://www.faostat.fao.org/site/339/default.aspx [10] http://www.assignmentpoint.com/science/zoology /agrisector of bangladesh.html