## 1 Introduction  
In agriculture, soil is directly involved in the growth of crops, providing nutrients and moisture and a stable base for roots. Therefore, continuous soil management is essential for the production of high-quality agricultural products, including improved crop productivity. Soil property analysis is also important [1; 2; 3; 4]. Soil property analysis is typically performed in a laboratory after the soil samples have been collected and utilizes a variety of chemical methods [5]. Although these methods are highly accurate in measuring soil properties, they are not efficient, as they consume large amounts of time, money, and labor. Therefore, for rapid soil property identification and testing, a technology that can quickly measure in the field is needed [6]. Diffuse reflectance spectroscopy (DRS) analyzes based on the interaction between incident light and the soil surface, mainly in the visible, near-infrared, and mid-infrared (VIS, NIR, and MIR) spectra at 400 - 700, 700 - 2500, and 2500 - 25,000 nm, respectively, with the VIS-NIR spectral region being suitable for measuring and predicting soil properties [7; 8; 9]. Using DRS, the reflected light depends on the physical and chemical properties of the soil, allowing for the simultaneous measurement of different soil properties [10]. In addition, [PERSON] et al. (2009) [9] developed an effective estimation model of soil properties within the NIR wavelength range rather than the VIS-NIR wavelength range. The duration and cost of soil property analysis can be reduced by developing a soil property prediction model, and in the future, real-time field measurements could be made using this model. According to [PERSON] et al. (2021) [11], soil properties can be predicted at a single level or multiple levels. However, studies using a single level are rare, and often, combined preprocessing techniques are conducted at multiple levels. According to [PERSON] et al. (2018) [12], the soil property predictive model depends on various factors, such as spectral preprocessing, calibration modeling, and data set size. Various analysis methods, such as machine learning (ML) algorithms, artificial neural networks (ANNs), support vector machines (SVMs), Cubist models, random forest (RF), and memory-based learning (MBL), could be used for the soil property predictive model. ML could be applied to complex nonlinear relationships between the predicted and response variables. An SVM as a kernel-based algorithm is transformed to maximize the distance of the nearest data points in the input classes into a high-dimensional space. The ANN algorithm learns through optimization until the differences between the observed and predicted values are minimized. The Cubist model is based on a classification and regression tree approach, where prediction is based on intermediate linear models formed at each tree node [13]. [PERSON] et al. (2019) [14] developed a soil property prediction model utilizing four analysis models, such as partial least squares regression (PLSR), neural network (NN), decision tree learning, and RF. Overall, they reported that the PLSR method could accurately estimate various physical and chemical soil properties. [PERSON] et al. (2017) [15] used PLSR and linear multitask learning (LMTL) analysis models to develop a prediction model of soil properties, such as nitrogen (N), phosphorus (P), potassium (K), pH, electrical conductivity (EC), organic matter (OM), and water content (WC). The PLSR model showed better results than the LTML model for most soil properties except P, pH, and WC. The PLSR analysis technique has shown good results in predicting various soil properties, but it is known that various factors, such as soil moisture content and soil properties, affect the model's accuracy [7; 8]. Factors such as soil moisture content and satellites, which affect the correlation between spectral reflectance and soil properties, can be improved using various forms of preprocessing [16]. Among the preprocessing techniques, [PERSON] et al.