Conversational AI for Cinnamon and Coffee Exports: Insights on Price and Yield
Keywords:
LSTM, SVM, Export Crops, Artificial Intelligence.Abstract
This research covers the development of an AI-powered chatbot that will help develop the agricultural industry in Sri Lanka by answering queries regarding coffee and cinnamon, besides giving weekly producer’s price predictions for them. It uses an SVM classifier that selects suitable responses from a given query in Sinhala, translates into English, generates the response, and then translates back to Sinhala for presentation. It implements an LSTM model to forecast prices of export crops from 2016 to 2022. It was observed that there is a great correlation between crop prices and the start date of the week they are valid, with a Pearson coefficient of over 0.70 for both coffee and cinnamon, while others are below 0.60. The chatbot returned to an accuracy rate of 70% in the classification of queries, while poor performance was obtained for harvest prediction due to a lack of sufficient data. The successful integration of predictive models and the chatbot proves the potential of AI in improving agricultural decision-making, productivity, and efficiency. This research consists of a Sinhala language-based chatbot, providing customized advisory services and weekly price predictions, contributing to localized technological advancements in Sri Lankan agriculture.
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