International Journal on Science and Technology

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Agri - Farm Assist

Author(s) J.Subba Rami Reddy, J.Karthik, G.T.P.Hemanth Reddy, Dr.D.Usha, Dr.T.Kiruba Devi
Country India
Abstract The paper presents an agricultural chatbot system enhanced with convotutional Neural Networks(CNNs) for accurate and efficient processing of farmer queries. Traditional chatbots often rely on rule-based or basic NLP models, which may struggle with domain specific vocabulary ,particularly in agriculture. By incorporating CNNs, the system improves understanding of complex agricultural terms and enables accurate intent classification and entity recognition, crucial for delivering precise information. The proposed model leverages CNN’s hierarical feature extraction capabilities to identify key phrases and agricultural-specific entities, such as crop names, pest types, and disease symptoms, from both text and voice inputs.The CNN-empowered chatbot integrates seamlessly with voice assistants,allowing hands-free voice-driven interactions for farmers,particularly in rural areas with low literacy rates. The chatbot processes spoken queries via automatic speech recognition(ASR)and responds with synthesized voice output,making agricultural guidance more accessible. The results indicate that CNN-based models outperform traditional NLP approaches in accuracy and usability.
Keywords Agriculture,chatbot,NLP,voice assistant CNN,queries.
Field Engineering
Published In Volume 16, Issue 1, January-March 2025
Published On 2025-03-31
Cite This Agri - Farm Assist - J.Subba Rami Reddy, J.Karthik, G.T.P.Hemanth Reddy, Dr.D.Usha, Dr.T.Kiruba Devi - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.2953
DOI https://doi.org/10.71097/IJSAT.v16.i1.2953
Short DOI https://doi.org/g9dgp3
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