## 2 Related Work  
The soil moisture and organic matter detection method through on-site sampling and laboratory analysis has proven to be the most reliable and accurate means [23; 24]. [PERSON] and [PERSON] used gold nanoparticles (AuNPs) and electrochemically reduced graphene oxide (ERGO) nano-hybrid composite membranes to prepare an all-solid nitrate-doped polypyrrole (PPy(NO) ion-selective electrode (ISE)) [29; 30]. The ISE-based soil nitrate-nitrogen (NO-N) in situ test is monitored in a three-stage laboratory column [31]. [PERSON] and [PERSON] used genetic algorithms to classify the qualitative levels of SN or installed wired or wireless sensor devices in the field for real-time soil monitoring [32]. [PERSON], [PERSON], and others designed and produced a composite polydimethylsiloxane (PDMS) device to test the amount of soil EC and nitrite [33].  
Many scholars also used near-infrared (NIR) spectroscopy technology for SN moisture determination. [PERSON] et al. combined the migration learning algorithm with near-infrared spectroscopy technology to establish a s SN information extraction model. The results showed that the model improved the efficiency of Please add academic editor if available. information extraction and obtained high-confidence feature extraction results [34]. [PERSON] et al. used NIRS to accurately predict soil macronutrients (N, P, K, S, Ca, and Mg) and micronutrients (Zn, Mn, and Cu) [35]. [PERSON] et al. developed an online TN concentration detector based on near-infrared spectroscopy technology by using absorbance data at 1680, 1550, 1375, 1245, 1130, and 1070 nm to establish an ELM model to identify soil nitrogen content [36]. The predictive model based on machine learning algorithms had strong analytical capabilities and robustness to nonlinear problems; it has been successfully applied to many fields [37; 38]. In soil detection, ELM was also widely used to estimate soil moisture, soil temperature, and SN and achieved high prediction accuracy [39; 40].  
In recent years, hyperspectral technology has been developed and increasingly used to determine soil organic matter and moisture content. [PERSON] et al. used hyperspectral technology to detect the OM and OC contents of forest surface soil [41]. [PERSON] et al. used hyperspectral technology to quantitatively estimate and compare the cadmium (Cd) concentration in standard and natural cadmium-contaminated soil samples [42]. However, these researchers only used the spectral information in the hyperspectral data for modeling and prediction. Hyperspectral technology combined with image technology can reflect the distribution of sample reference values spatially; thus, the corresponding decisions are based on the distribution map. This method has been successfully applied to many fields [43; 44], proving that it can feasibly be used to detect SN content and obtain content distribution maps with spectral and image information [45; 46].  
In this article, hyperspectral technology was used in combination with machine learning to identify SN and moisture, which can save manpower and avoid large-area sensor deployment and achieve non-contact detection. The method can be applied to a variety of platforms, such as portable hyperspectral or unmanned aerial vehicles.