Jinghe Zhou
ABSTRACT
Starting from the Internet of Things technology, based on smart agriculture, this paper uses sensor networks to collect data, and puts forward opinions on maize growth monitoring based on rapid and accurate prediction of maize yield based on variational mode decomposition (VMD) and neural network (BP), to meet farmers' high requirements for the practicality, simplicity, and reliability of smart agriculture. An IoT-based corn yield prediction and analysis system was designed. The system collects environmental data (such as soil temperature, humidity, light intensity, etc.) during the growth process of corn by building a sensor network and cloud platform, and processes and analyzes the data in real time. Using the VMD-BP model algorithm, combining historical and real-time data, future corn yield predictions are made and presented to users through a visual interface. Experimental results show that the system has chief advantages in prediction accuracy and real-time and can effectively improve the efficiency and yield of maize planting. The research results of this paper will help promote the application and development of IoT technology in the agricultural field.
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Index Terms
- Research on VMD-BP Intelligent Agriculture Corn Yield Prediction in the Internet of Things Environment
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