AI-Powered Crop Recommendation System with Chatbot Integration

Abstract

Farming involves cultivating the soil, producing crops, and keeping livestock. The agricultural sector plays a crucial role in a country's economic growth. Machine learning (ML) generates crop recommendations based on factors such as NPK (Nitrogen, Phosphorus, Potassium), soil pH, and climatic variables. Various ML models are used for this purpose. Studies have shown that evaluating individual datasets separately for each crop category leads to better predictions. Additionally, a chatterbot is being developed to assist farmers in their farming practices. It utilizes natural language processing (NLP). It serves as an interactive virtual assistant that provides accurate answers to farmers' queries related to agriculture. The web-based application features a Farmer and Admin login for privacy purposes it aims to offer farmers a comprehensive understanding of agricultural practices. By leveraging the Django framework and chatterbot libraries, the chatbot provides an efficient interface for farmers to communicate and receive valuable insights for making informed decisions that can improve crop and livestock productivity. Overall, the integration of AI technologies like chatbots and ML algorithms holds the potential to revolutionize crop recommendations for farmers, providing valuable insights for optimal fertilizer usage.