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Research Paper | Computer Science & Engineering | India | Volume 7 Issue 12, December 2018
Prediction of Sugarcane Yield using KNN and KNN Plus Clustering Algorithms
M. Naveen Kumar, Dr. M. Balakrishnan
Abstract: Traditionally, the crop analysis and agricultural production predictions were done based on statistical models. However, with the climate of the world changing to drastic degrees, these statistical models have become very ambiguous. Hence, it becomes prudent that we resort to other less vague methods. Through a traditional model, user interacts primarily with mathematical computations and its results and can help to solve well-defined and structured problems. Whereas, in a data driven model, user interacts primarily with the data and helps to solve mainly unstructured problems. At this point, enters the concept of Machine Learning. In this work we tried to find a new approach to reduce the input feature to reduce the processing power needed. In this work we have attempted at predicting the agricultural outputs of sugarcane production in Telangana Region area by implementing a KNN based machine learning model. Through this model, we tried to predict the approximate crop yield based on various parameters values analyzed for a particular season and area. Results from the simulated studies showed that the statistical models can roughly simulate pre harvest yield forecast of sugarcane under telangana region. This paper deals with study of KNN and clustered KNN algorithms which are suitable for the available predictors and predictions and to perform analysis, cleaning, pre processing and feature selection on the data and which will be very much useful to the researchers, policy and decision makers
Keywords: Sugarcane, Yield prediction, KNN, Clustered KNN
Edition: Volume 7 Issue 12, December 2018,
Pages: 110 - 113
How to Cite this Article?
M. Naveen Kumar, Dr. M. Balakrishnan, "Prediction of Sugarcane Yield using KNN and KNN Plus Clustering Algorithms", International Journal of Science and Research (IJSR), https://www.ijsr.net/get_abstract.php?paper_id=ART20193317, Volume 7 Issue 12, December 2018, 110 - 113
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