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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM44877

Deep Learning-Based Agricultural Chatbot with Multi-Language Support for Small Farmers

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM44877

Volume:

9 |

4 |

Apr 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM44877

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

YM

YAMINI M

SS

SHARMILA S

SB

SUSHMITHA BA

SM

SWETHA M

UKBS

U K BALAJI SARAVANAN

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Abstract

Agriculture is an important sector and small-scale farmers typically lack timely and accurate information to increase their productivity. This project presents a Deep Learning Based Agricultural Chatbot with Multi Language Support to assist the small farmers in real time agriculture practice guidance, pest control, weather updates, soil dedication. Deep learning techniques including Natural Language Processing (NLP) models are used by the chatbot to understand and answer to the farmer queries accurately. This chatbot is multilingual, and the farmer can converse with it in any language, and it mainly relies on pre-trained deep learning models and a huge agricultural knowledge base to give out insights [1][2]. To make the system a reliable virtual assistant for the farmers, extensive datasets are trained to increase response accuracy [3]. This chatbot helps to bridge the knowledge gap in agriculture towards small farmers to provide them with data driven insights that will help them to maximize their yield and manage their risks effectively [4]

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Cite This Article

YAMINI M, SHARMILA S, SUSHMITHA BA, SWETHA M, U K BALAJI SARAVANAN, "Deep Learning-Based Agricultural Chatbot with Multi-Language Support for Small Farmers," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 4, Apr 2025. https://doi.org/10.55041/IJSREM44877

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Publication Date

4/17/2025

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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