## 1 Introduction  
An agricultural greenhouse can provide relatively suitable growing conditions for crops in all seasons. An agricultural greenhouse is the main way of facilitating precise agriculture and efficient agriculture production [1]. There are 1.96 million hectares of single-slope solar greenhouses in northern China, which are the main type of greenhouse for vegetable production. The walls of most solar greenhouses consist of rammed soil, and the total area of most solar greenhouses range from 300 m\({}^{2}\) to 1200 m\({}^{2}\)[2, 3]. For many greenhouse environmental parameters, temperature and relative humidity in greenhouses are the main factors affecting the healthy growth and yield of crops, and are the key to achieving the relative stability of its internal environment [4]. The core of greenhouse environmental system modeling is the relationship between environment parameters and crop production. Those greenhouse environmental parameters have been documented to be sensitive to greenhouse structure and outdoor weather variation, which were considered dynamic and complex systems [5; 6]. In recent years, a lot of studies have been conducted on greenhouse environmental modeling. Modeling methods for greenhouses can be divided into mechanism model, time series model and artificial intelligence model [1], those mechanism methods were dependent on physiological processes, and they represent crop growth, and yield as a function of several climate and physiological parameters [7]. A large number of environmental parameters need to be considered when building a mechanism model, and the unmeasurable environmental parameters were estimated or calculated. Because the other two models do not focus on the laws and principles of physics in the greenhouse, they are called Black-Box models. These two methods provide rapid and accurate results for greenhouse environmental prediction and regulation, for example, the temperature, humidity or reference evapotranspiration inside the greenhouse, and the planning of irrigation strategies [8]. A Kalman filter or extended Kalman filter is applicable to linear systems and has been introduced in the literature. It is not easy to derive the Jacobian matrix for the nonlinearization process, which often leads to major difficulties in implementation [9; 10]. With the rapid development of computer technology and artificial intelligence technology, many excellent algorithms have been proposed to solve various complex problems involved in the greenhouse environment model, such as Kth-nearest neighbor (KNN), back-propagation (BP) neural networks, artificial neural network (ANN) [11], multi-layer perceptron (MLP), classification and regression tree (CART), support vector machines (SVM), wavelet neural network (WNN) and radial basis function (RBF) [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23]. These algorithms are effective for the construction of temperature field, the solar radiation field, and the other nonlinear physical phenomena in greenhouse. However, those approaches still have some drawbacks, for example, poor applicability, overfitting, poor robustness and poor stability to actual strongly coupled and complex systems. Deep learning (DL) can solve complex problems particularly well and quickly, due to the more complex models used, which also allow massive parallelization. The deep learning technology including recurrent neural networks (RNN), long short-term memory (LSTM), deep learning neural networks (DLNN) and convolutional neural networks (CNN) have been used to deal with the complex systems and prediction problems in the agricultural industry. Time series prediction based on LSTM models has been widely applied in various fields in recent years, including the prediction model of greenhouse environmental parameters. [PERSON] et al. [8] developed a model to predict indoor temperature, relative humidity, and CO\({}_{2}\) concentration of a greenhouse using outdoor weather data and historical data, and the model was used to irrigation management of greenhouse tomatoes.