In order to deeply integrate smart agriculture with the new generation of information technology, we have designed a system that can collect crop growth data and environmental data represented by soil moisture and temperature for digital modeling. At the same time, to make full use of artificial intelligence to extract effective information from big data to help agricultural planting and decision-making, we combine the data collected by sensors with the existing knowledge about agriculture on the internet to build an agricultural knowledge base with entities and relationships. A multi-hop question-answering model based on a knowledge base is designed to guide agricultural production. We are taking 100-hectare experimental farmland as an example, introducing the deployment method of agricultural sensors, and focusing on designing a multi-hop question-answering model for the knowledge base. Our question-answering model achieves SOTA on three publicly available datasets. Moreover, 100% accuracy was obtained on the 3-hop test set of MetaQA.