  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.
