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Using Artificial Neural Networks (ANNs) to Improve Agricultural Knowledge Management System (KMS)

Using Artificial Neural Networks (ANNs) to Improve Agricultural Knowledge Management System (KMS)

Mriganka Mohan Chanda, Neelotpaul Banerjee, Gautam Bandyopadhyay
Copyright: © 2020 |Volume: 16 |Issue: 2 |Pages: 18
ISSN: 1548-0666|EISSN: 1548-0658|EISBN13: 9781799804949|DOI: 10.4018/IJKM.2020040106
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MLA

Chanda, Mriganka Mohan, et al. "Using Artificial Neural Networks (ANNs) to Improve Agricultural Knowledge Management System (KMS)." IJKM vol.16, no.2 2020: pp.84-101. http://doi.org/10.4018/IJKM.2020040106

APA

Chanda, M. M., Banerjee, N., & Bandyopadhyay, G. (2020). Using Artificial Neural Networks (ANNs) to Improve Agricultural Knowledge Management System (KMS). International Journal of Knowledge Management (IJKM), 16(2), 84-101. http://doi.org/10.4018/IJKM.2020040106

Chicago

Chanda, Mriganka Mohan, Neelotpaul Banerjee, and Gautam Bandyopadhyay. "Using Artificial Neural Networks (ANNs) to Improve Agricultural Knowledge Management System (KMS)," International Journal of Knowledge Management (IJKM) 16, no.2: 84-101. http://doi.org/10.4018/IJKM.2020040106

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Abstract

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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