Comparative Study of Different Intent Classification Algorithms for Agriculture Question and Answer Task

This research examines the effectiveness of various machine learning models in accurately classifying the intent of agricultural queries using fundamental machine learning techniques. The study analyzes two curated datasets: the QUERY dataset, containing 2,853 queries and their corresponding intents, and the ANSWERS dataset, which includes crops and their associated intents, covering 21 intent categories across 30 different crops. The main objective is to evaluate the performance of traditional machine learning models on this dataset to enable accurate intent classification, facilitating responsive interactions via the QA system, which delivers data-driven answers to farmers' queries. Notably, the Support Vector Classifier demonstrated the best performance with an accuracy of 92<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup>, closely followed by the Linear Discriminant Analysis Classifier. Using the Support Vector Classifier, the QA system efficiently retrieves responses from the ANSWERS dataset. This paper provides a comprehensive analysis of the performance of various algorithms for the question-answering (QA) task in the agricultural domain. The evaluation is based on pre-modelled classification reports from Sklearn's metrics package, ensuring rigorous analysis and detailed insights into the effectiveness of each approach.