用时空图神经网络预测天气并推荐作物，还配聊天机器人答疑。
Crop Recommendation and Agricultural Query Answering System Using Spatio-Temporal Graph Neural Networks and Hybrid Retrieval Augmentation
- 用STGCN模型分析1359个地点气候数据，预测未来30天天气。
- 天气与土壤数据结合，生成符合本地条件的作物推荐方案。
- 内置农业问答机器人，支持农民自然语言提问，适合农村使用。
本文提出一个统一系统，支持精准农业，整合先进天气预测、作物推荐和面向农户的问题回答功能。我们设计了两种深度学习模型——基于Transformer的图神经网络与时空图卷积网络（STGCN），利用尼泊尔1,359个地点的数据，预测未来30天的天气状况。STGCN在准确性上优于Transformer模型（均方误差约0.011对比0.013），有效捕捉气候数据中的时空依赖关系。将预测结果与静态土壤属性（如pH值、湿度、有机质含量）结合，通过评分算法匹配每种作物的理想生长条件，生成本地化作物推荐。此外，我们开发了一个检索增强生成（RAG）聊天机器人，利用领域特定农业文档回答农民的自然语言问题。整个系统通过移动端应用部署，提供实时建议与对话支持。用户反馈表明系统易用且相关，尤其适用于缺乏个性化农事指导的农村地区。总体而言，该方法展示出融合机器学习与本地农业数据，可为农民提供可操作洞察，提升决策水平、增产并增强对气候波动的适应力。
原文摘要 · Abstract (English)
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.
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