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Analysis of Optimum Crop Cultivation using Fuzzy System

机译:基于模糊系统分析的最佳作物栽培方法。

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In this paper we have proposed a system that will be able to analyze the Optimum Crop Cultivation of Bangladesh based on the knowledge of Neuro-Fuzzy System (NFS). The Neuro-fuzzy system is the collection of two techniques: fuzzy logic and the neural network. The system can compute the yield of a certain crop by using the value of humidity, temperature and rainfall. By using this system farmer will be able to increase agricultural productivity. Hence, this will have an overwhelming impact on poverty alleviation, boosting employment rate, human resource development and food security. The dataset that we used to train our system was collected from the official website of Bangladesh Bureau of Statistics. It contains the humidity, temperature and rainfall values of thirty-three districts of Bangladesh that produced the most crops from the year 2007-2013. We considered a few major crops of Bangladesh, i.e., Rice (Aus, Amon, Boro), Wheat and Potato. By using this system, farmers can harvest maximum production of crops throughout the various seasons in the year.
机译:在本文中,我们提出了一个系统,该系统将能够基于神经模糊系统(NFS)的知识来分析孟加拉国的最佳作物栽培。神经模糊系统是两种技术的集合:模糊逻辑和神经网络。该系统可以通过使用湿度,温度和降雨量的值来计算某种农作物的产量。通过使用该系统,农民将能够提高农业生产率。因此,这将对减轻贫困,促进就业率,人力资源开发和粮食安全产生巨大影响。我们用来训练系统的数据集是从孟加拉国统计局的官方网站上收集的。它包含了孟加拉国33个地区的湿度,温度和降雨值,这些地区在2007-2013年间的农作物产量最高。我们考虑了孟加拉国的几种主要农作物,即水稻(澳大利亚,阿蒙,博罗),小麦和马铃薯。通过使用该系统,农民可以在一年中的各个季节收获最大的农作物。

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