A Reversible Automatic Selection Normalization (RASN) Deep Network for Predicting in the Smart Agriculture System

Due to the nonlinear modeling capabilities, deep learning prediction networks have become widely used for smart agriculture. Because the sensing data has noise and complex nonlinearity, it is still an open topic to improve its performance. This paper proposes a Reversible Automatic Selection Normalization (RASN) network, integrating the normalization and renormalization layer to evaluate and select the normalization module of the prediction model. The prediction accuracy has been improved effectively by scaling and translating the input with learnable parameters. The application results of the prediction show that the model has good prediction ability and adaptability for the greenhouse in the smart agriculture system.

Automated summary

This paper proposes a Reversible Automatic Selection Normalization (RASN) network to improve the performance of deep learning prediction networks widely used in smart agriculture. By integrating normalization and renormalization layers, and scaling and translating the input with learnable parameters, the accuracy of the prediction has been effectively improved. The application results show that the model has good prediction ability and adaptability for greenhouses in smart agriculture system.