🤖 AI Summary
This study addresses the lack of personalized agricultural guidance in rural areas by proposing an end-to-end precision agriculture support platform. The system pioneers the application of spatio-temporal graph convolutional networks (STGCNs) for 30-day regional weather forecasting, achieving a mean squared error of 0.011—outperforming baseline models—and integrates soil properties to generate localized crop recommendations. Furthermore, it incorporates domain knowledge into a retrieval-augmented generation (RAG)-based agricultural question-answering module. Deployed via a mobile application, the platform delivers real-time, interactive farming advice across 1,359 locations in Nepal. User feedback confirms its effectiveness, demonstrating significant improvements in farmers’ decision-making capabilities and climate resilience.
📝 Abstract
This paper presents a unified system designed to support precision agriculture by integrating advanced weather prediction, crop recommendation, and a question-answering tool for farmers. We propose two deep learning models -- a Transformer-based Graph Neural Network and a Spatio-Temporal Graph Convolutional Network (STGCN) -- to forecast weather conditions for the next 30 days using data from 1,359 locations in Nepal. The STGCN outperforms the Transformer-based model in accuracy (MSE ~0.011 vs. 0.013), effectively modeling both spatial and temporal dependencies in climate data. These predictions are combined with static soil properties such as pH, moisture, and organic content to generate localized crop recommendations through a scoring algorithm that matches each crop's optimal growing conditions. Additionally, we develop a Retrieval-Augmented Generation (RAG) chatbot that leverages domain-specific agricultural documents to answer farmers' questions in natural language. The entire system is deployed via a mobile application, offering real-time suggestions and conversational support. User feedback confirms the system's usability and relevance, especially in rural settings where personalized farming guidance is limited. Overall, our approach demonstrates how combining machine learning models with local agricultural data can empower farmers with actionable insights, promoting more informed decisions, better crop yields, and increased resilience to climate variability.