关键词: 温室农业, 小气候, 神经网络, 模型预测, 优化算法, 深度学习

Abstract: In recent years, facility cultivation technology, mainly in the form of solar greenhouses, and the major breakthroughs made by neural networks in the fields of speech recognition, computer vision, and sequence classification as effective tools for realizing time series prediction, have resulted in the increasing application of neural network technology in greenhouse microclimate prediction. This paper summarizes and describes the application of different types of neural network models in the prediction of greenhouse microclimate according to the development sequence of neural networks. The optimization of the network model is introduced in detail. However, the current neural network model for greenhouse microclimate prediction has some limitations, such as that a single model input parameter cannot consider the overall environmental changes of the greenhouse, that a single model structure leads to insufficient robustness, and that model testing methods are inappropriate for deep models and difficult to deploy in the field. This paper put forward suggestions to establish a hybrid model, improve model testing methods, and optimize the network structure of deep models. This paper expects to provide a reference for further research on intelligent control of greenhouses.

Key words: facility agriculture, microclimate, neural network, model prediction, optimization algorithm, deep learning