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Advancements in information sciences can play a vital role in strengthening the nation&#x2019;s sustainable agriculture goals. In this direction, we propose a framework for a text-based query-response generation system to cope with the demand for timely help to the nationwide Indian farmers. One of the major challenges in designing such systems is constructing a knowledge base that can answer plant-protection-related questions from a diverse population of farmers. To tackle this problem, the past eight years&#x2019; call-log records from the countrywide farmers&#x2019; helpline network are collected and processed to construct the required knowledge base. Additionally, three response-retrieval models with approximate matching and spatial-based searching functionality are developed to administer the user input questions and extract relevant answers from the base. To validate the performance of the proposed framework, a diversified question bank consisting of 755 queries covering 151 crops in India is compiled. Three metrics (Accuracy Percentage, Crop-weighted Performance Score, and Average Response-retrieval time) are considered for the models&#x2019; assessment. Experimental results show that AgriResponse is a practical framework in real-world applications, with the different retrieval models useful for different scenarios.

AgriResponse: A real-time agricultural query-response generation system for assisting nationwide farmers