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Analysis and prediction of smart data using machine learning

机译:使用机器学习的智能数据分析与预测

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In the field of agriculture, Machine Learning had been one of the most important technologies. The need aroused as the sensor technologies were proved to be advantageous in agricultural industry. Various sectors like food safety and breeding had its contribution, because agriculture got improvised by that. The data on agriculture were taken from Tamil Nadu data set. A comparison of consecutive years (2009-2013) was made in the production of crops among different seasons like Rabi, Kharif. The data available helped in the prediction of crop yield. Thereby, its analysis allowed farmers as well as companies for retrieving the value from certain data and also improved productivity. The Indian economy basically relied on the agricultural sector. Agriculture products needed a variety of protection like protection from insects, protection against rodents and many such undesired attacks in the field of agriculture. Growing status of crops was tracked by segregating, recognizing and measuring areas of different crops in Tamil Nadu and also estimated production early in the year. One of the biggest problems to be tackled is agricultural planning. Crop selection was a major issue where cropping using available resources was a major concern. The main aim of this paper is to predict crops production using Machine Learning Algorithms making use of given data set. The various crops production was compared in Rabi and Kharif seasons and also for the whole year from 2009-2013.
机译:在农业领域,机器学习一直是最重要的技术之一。被证明在农业产业中被证明被称为传感器技术的需求。不同的部门,如食品安全和育种有其贡献,因为农业得到了这一点。农业数据来自泰米尔纳德数据集。连续几年(2009-2013)的比较是在rabi,kharif这样的不同季节中生产作物。可用的数据有助于预测作物产量。因此,其分析允许农民以及公司从某些数据中检索价值,并提高生产力。印度经济基本上依靠农业部门。农业产品需要各种保护,如保护等保护,防止啮齿动物和农业领域的这种不希望的攻击。通过在泰米尔纳德邦的不同作物的分离,识别和测量不同作物的地区来跟踪作物的日益增长的作品状态,并在年初估计生产。待解决的最大问题之一是农业规划。作物选择是使用可用资源裁剪的主要问题是一个主要问题。本文的主要目的是使用机器学习算法预测使用给定数据集的机器学习算法预测作物生产。在2009 - 2013年的rabi和Kharif Seasons中,在rabi和kharif季节比较了各种作物生产。

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