Farmer-Bot: An Interactive Bot for Farmers

TL;DR

This paper presents Farmer-Bot, an interactive WhatsApp-based chatbot for Indian farmers, leveraging NLP and the Kisan Call Center dataset to improve agricultural information access and address existing communication constraints.

Contribution

It introduces a novel WhatsApp chatbot using NLP techniques and KCC data to enhance farmer communication and information dissemination in India.

Findings

KCC dataset can be effectively used for chatbot development

The NLP model achieves high semantic similarity accuracy

Farmer-Bot improves accessibility of agricultural advice

Abstract

The Indian Agricultural sector generates huge employment accounting for over 54% of countrys workforce. Its overall stand in GDP is close to 14%. However, this sector has been plagued by knowledge and infrastructure deficit, especially in the rural sectors. Like other sectors, the Indian Agricultural sector has seen rapid digitization with use of technology and Kisan Call Center (KCC) is one such example. It is a Government of India initiative launched on 21st January 2004 which is a synthesis of two hitherto separate sectors the Information Technology and Agriculture sector. However, studies have shown to have constrains to KCC beneficiaries, especially in light of network congestion and incomplete knowledge of the call center representatives. With the advent of new technologies, like first-generation SMS based and next-generation social media tools like WhatsApp, farmers in India are digitally more connected to the agricultural information services. Previous studies have shown that the KCC dataset can be used as a viable alternative for Chat-bot. We will base our study with the available KCC dataset to build an NLP model by getting the semantic similarity of the queries made by farmers in the past and use it to automatically answer future queries. We will attempt to make a WhatsApp based chat-bot to easily communicate with farmers using RASA as a tool.