Aiming at the problems of current farmland sensor nodes information query and ontology knowledge construction， this paper conducts an analysis of the farmland moisture collection system. It proposes a lightweight semantic sensor network ontology and extracts the relationships from farmland information data. Additionally， it designs the FSSN ontology annotation method and utilizes Tf-Idf algorithm to analyze the semantic weight of farmland Ontology. FSSN-SDRM algorithm is proposed to construct the farmland moisture lightweight ontology model， analyze the growing environment of crops， set suitability reasoning rules， and the Jena API is employed for reasoning the annotated ontology model. According to the soil moisture information collected by the farmland sensor nodes， experiments are conducted on the lightweight middleware NVIDIA TX2 platform to query the correctness of reasoning information in the database， and compare the response time of the equipment. The experimental results show that lightweight FSSN annotation ontology compresses the average response time to 81 ms， which is 41.4% shorter than the uncommented ontology， and the time in TX2 platform is 12.81% shorter than that of the host side. The ontology model can quickly and accurately judge the suitability of crop growth environment and provides new ideas for agricultural information production.