Abstract
In the field of agriculture where farmers and agri-businesses have to decide on countless matters every day and complexities include different factors. The exact yield estimate of various crops included in the planning is a critical problem for agricultural planning purposes. For realistic and effective solutions to this problem, data mining techniques are required approach. Big data was an obvious aim for agriculture. Environmental conditions, soil fluctuations, input levels, combinations of commodity prices and the use of knowledge and support for important agriculture decisions have made farmers all the more relevant. This paper focuses on an overview of farm data and classifies the crop using the data mining methods of Naïve Bayes, SMO, Decision Table, J48 and k-means. Results show that before classification, clustering is useful.
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Bissa, A., Patel, M. (2021). An Adjustment to the Composition of the Techniques for Clustering and Classification to Boost Crop Classification. In: Singh Pundir, A.K., Yadav, A., Das, S. (eds) Recent Trends in Communication and Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-0167-5_13
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DOI: https://doi.org/10.1007/978-981-16-0167-5_13
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