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Firstly, self-developed monitoring equipment was used to realize real-time acquisition, format conversion and pretreatment of the physicochemical parameters of inputs, and real-time communication with the cloud platform server. In this process, LoRa technology was adopted to solve the wireless communication problems between long-distance, low-power, and multinode environments. Secondly, a deep belief network (DBN) model was used to learn unsupervised physicochemical parameters of input products and extract the input features. Finally, these input features were utilized on the softmax classifier to establish the classification model, which could accurately predict the varieties of agricultural inputs. The results showed that when six kinds of pesticides, chemical fertilizers, and other agricultural inputs were predicted through the system, the prediction accuracy could reach 98.5%. 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