• Susheel Sundarraj Gounder

Presently we receive around 5 lakh calls on our krushi call center (KCC) every month. This shows that our farmers want relevant information today and are willing to ask for it. However, expanding call centers is very expensive and requires huge capacity building. That is why we must develop intelligent chatbots where farmers can ask in their local language any query and our bot will give a meaningful reply. The project will involve a lot of natural language processing because we can’t expect farmers to type commands to our bot (and anyways no one likes to talk to a machine!). So we can’t restrict the farmer’s interaction with our bot as press 1 for this, press 2 for this and so on. We have to make it as human-like as possible. Our chatbot will benefit from the localized context information about the farmer. Example, there would be a dedicated chatbot for say paddy growers in Seemandhra or orange growers of Jhalawar which will benefit from the specific knowledge there. For example, it would know what kind of varieties farmers typically grow, what are the diseases they are prone to, what are the solutions, what are the market prices there, what is the weather information and so on. This would also make our task a lot simpler and our bot more intelligent. Initially we are just looking to develop a bot for a specific context to give a proof of concept.

NLP, Agriculture, chatbot

"Agrobot : An agricultural advancement to enable smart farm services using NLP", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 7, page no.d445-d454, July-2021, Available :http://www.jetir.org/papers/JETIR2107448.pdf

"Agrobot : An agricultural advancement to enable smart farm services using NLP", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 7, page no. ppd445-d454, July-2021, Available at : http://www.jetir.org/papers/JETIR2107448.pdf