Wheat Yield Prediction using Data Mining
Manan Kawatra1, Jagdeep Singh2, Sachin Bagga3

1Manan Kawatra, Information Technology, Guru Nanak Dev Engineering College, Ludhiana, India.
2Prof. Jagdeep Singh, Information Technology, Guru Nanak Dev Engineering College, Ludhiana, India.
3Prof. Sachin Bagga, Information Technology, Guru Nanak Dev Engineering College, Ludhiana, India. 

Manuscript received on 15 August 2019. | Revised Manuscript received on 25 August 2019. | Manuscript published on 30 September 2019. | PP: 989-993 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4057098319/19©BEIESP | DOI: 10.35940/ijrte.C4057.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: These days peasants are facing problems in producing the yield of any crop due climatic changes which are unpredictable. Therefore, to provide improvement in agriculture a well organized and approach is needed to predict the yielding of crop on the basis of which planning can be done. The prediction must be good enough to support the peasants to take appropriate actions to get the apt amount of the crop by selling it. This work aims to provide prediction with high accuracy and less RMSE. It deals with the prediction of the wheat crop yielding using data mining techniques. It helps in predicting crop yield using previous data with the help of parameters like Rainfall and Temperature. It consists of clustering which has been done with the help of k-means, extracting features with independent component analysis, instance selection using moth flame optimization and the prediction and classification has been performed with Linear Discriminant Analysis. The technique is applied MATLAB. The performance is evaluated using mean square error rate and prediction of yielding of wheat is also calculated. The error must be less to get more accuracy for the predicting rate.
Keywords: Data-mining, Linear Discriminant Analysis (LDA), K-means clustering ,moth flame optimization, wheat yielding.
Scope of the Article:
Regression and Prediction