## 1 Introduction  
_Pinus massoniana_ is a pioneer tree species that plays an essential role in reforestation work in South China due to its wide distribution, rapid growth, and strong environmental adaptability [1]. The cultivation of _Pinus massoniana_ is not easy, and the quality of its seedlings is directly related to the success of reforestation. The moisture and nitrogen content in leaves of _Pinus massoniana_ seedlings are closely related to plants' physiological functions and can reflect the health status of seedlings [2]. Therefore, during the initial stage of reforestation, to evaluate the health status of _Pinus massoniana_ seedlings, the most rapid method is to measure the moisture and nitrogen content in _Pinus massoniana_ seedling leaves. Near-infrared spectroscopy (NIRS) is widely used to analyze the components of various plants, such as the moisture content of wheat and soybean [3, 4], the nitrogen content of _Pinus massoniana_ and rice [5, 6], and the chlorophyll content of sunflower [7] by capturing the hyperspectral differences caused by differences of frequency doubling and frequency vibration of molecular groups in plants. [PERSON] et al. [8] applied multi-spectral technology and the back propagation (BP) neural network to predict the moisture content in corn leaves. However, due to the use of multi-spectral data and an insufficient volume of data, the prediction accuracy was low. [PERSON] et al. [9] used NIRS technology, combined with genetic algorithm screening, to construct a rice moisture content predictive model. However, the method predicts based on raw characteristic wavelength, which is only suitable for the measured objects with prominent characteristics and is challenging to apply to measured objects with insignificant characteristics. [PERSON] et al. [10] used hyperspectral technology to detect moisture content in camellia seeds and applied a radial basis function neural network as the leading predicting mean. This method integrates neural networks and hyperspectral technology, but the network structure is simple, and the prediction result is not sufficiently accurate. Measurement models commonly used in NIRS include multi-linear regression (MLR) [11], partial least squares regression (PLSR) [12], support vector regression (SVR) [13], and artificial neural network (ANN) [14], among others [15]. MLR and PLSR are linear regression models, which are suitable for describing strong linear relationships. Traditional ANN and SVR introduce nonlinearity through a non-linear activation function and kernel function, respectively, but cannot solve problems with a high degree of abstraction; for the latter case, the deep neural network was devised. [PERSON] et al. combined the PLSR method based on stack self-coding with hyperspectral imaging technology to realize the rapid non-destructive testing of the solid soluble content in green plumes [16]. Although this more in-depth learning network has better nonlinearity, due to the increase of the network's depth, the gradient of the backpropagation loss function may disappear during training, so that the weight of the network cannot be effectively adjusted. He's team proposed the residual network (Res Net) in 2016, which forms a unique residual block structure by short cut (SC) [17], so that the error of the network can cross the multi-layer network during reverse propagation, overcoming the problem of gradient disappearance, and improve the depth of the network. Res Net application has achieved several research results, effectively solving practical problems. [PERSON] et al. used a deep Res Net for fault diagnosis and defect analysis of a photovoltaic array [18]; [PERSON] et al. applied Res Net to build a low-dose computed tomography image reconstruction method [19]; [PERSON] et al. built a bearing fault detection method using Res Net, which can locate the defect position accurately by the bearing vibration signal [20].