Bayesian Networks for Improved Estimation of Paddy Crop Production
Keywords:
Agriculture, Bayesian networks, clustering, classifiers, yield estimation, data miningAbstract
In India’s food security, Paddy crop production has a prominent role by dispensing more than 40% of production of crop. Depends on climatic situation, paddy crop gives better production. Changes in seasonal climatic situations like low temperature or rainfall have negative impact of crop yielding. For improving the decision making capacity of farmers and stakeholders by considering the agronomy and crop choice, effective methods are developing for predicting the crop productivity under different climatic situation. The aim of this paper is estimating the paddy crop yield of Anantapur district, India. Anantapur was selected for this report accordingly considering the information existing in the Indian administration chronicles with different atmospheric and production predications like area production, precipitation, rainfall, minimal temperature, intermediate temperature, maximal temperature, evapotranspiration of crop and production from 2007 to 2012 of the Kharif season which is from June to November are selected. The dataset utilizing was processed using alled the tool WEKA. Clustering is performed by using k-means cluster. Classifiers named naïve bayes and bayesnet are used in this report. Proposed methodology gives better performance using bayesnet instead of naivebayes classifier for the dataset.References
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Copyright (c) 2023 Kadamba Pavani, Halavath Balaji, N Ch S N Iyengar
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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