利用人工神经网络改进农业知识管理系统

M. Chanda, Neelotpaul Banerjee, Gautam Bandyopadhyay
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引用次数: 1

摘要

农业是印度经济的一个重要部门。本文试图从理论上探讨农业知识管理系统(KMS)在各种农业微灌技术方面的发展,以及该国不同地区的相关作物/区域特定农业实践,因为观察到这对于更广泛的农民,农业科学家,经济学家和该领域的其他利益相关者的总体利益是非常必要的。进一步观察到,人工神经网络(ann)作为软计算技术的一部分,可以用作有效管理农业各子部门的KMS工具。在此背景下,已有研究表明,利用人工神经网络作为KMS工具,可以借助各种相关参数的过去数据准确预测印度粮食谷物的年度估计产量,从而提高上述农业KMS应用的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Artificial Neural Networks (ANNs) to Improve Agricultural Knowledge Management System (KMS)
Agriculture is an important sector of the Indian economy. In the present paper an attempt has been made to theoretically explore the development of an agricultural knowledge management system (KMS) in respect of various micro irrigation techniques for agriculture, as well as relevant crop-/region-specific agricultural practices in different regions of the country, as the same has been observed to be very much necessary for the overall benefits of wider cross section of farmers, agricultural scientists, economists, and other stakeholders in the domain. It is further observed that artificial neural networks (ANNs), which are a part of soft computing techniques, can be used as a KMS tool for effective management of various sub sectors of agriculture. In this context, it has been shown that use of ANNs as a KMS tool can improve the effectiveness of applications of the above mentioned agricultural KMS by accurately forecasting the year-wise estimated yield of food grains of India with the help of past data of various relevant parameters.
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