Analysis of Optimum Crop Cultivation using Fuzzy System

2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS) Pub Date : 2016-06-26 DOI:10.1109/ICIS.2016.7550759

Fahim Jawad, Tawsif Ur Rahman Choudhury, Asif Sazed, S. Yasmin, Kanaz Iffat Rishva, Fouzia Tamanna, R. Rahman

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引用次数: 17

Abstract

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.

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应用模糊系统分析作物最优栽培

本文提出了一种基于神经模糊系统(NFS)知识的孟加拉国最佳作物种植分析系统。神经模糊系统是模糊逻辑和神经网络两种技术的集合。该系统可以利用湿度、温度和降雨量的值来计算某一作物的产量。通过使用这个系统，农民将能够提高农业生产力。因此，这将对减轻贫困、提高就业率、开发人力资源和粮食安全产生巨大影响。我们用来训练系统的数据集来自孟加拉国统计局的官方网站。它包含了孟加拉国33个地区的湿度、温度和降雨量，这些地区从2007年到2013年生产了最多的作物。我们考虑了孟加拉国的几种主要作物，即水稻(Aus, Amon, Boro)，小麦和马铃薯。通过使用这个系统，农民可以在一年中的各个季节收获最大的作物产量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS)

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