# Survey for Soil Sensing with IOT and Traditional Systems
## 2 Tradicitonal Soil Sensing System
### Soil Macronutrients (N, P and K)  
Essential soil macronutrients, namely nitrogen, phosphorus, and potassium, are vital for plant and crop vitality [57]. Not only does accurate macronutrient detection enhance plant growth and crop yield [58], but it also mitigates risks associated with over-fertilization and groundwater contamination [59]. In [33], the researchers target the measurement of six soil properties: N, P, K, soil pH, magnesium (Mg), and calcium (Ca). Utilizing a spectrometer in the NIR region (1000-2500 nm), they gather spectral data and post certain preprocessing steps. This study also juxtaposed PCR (Principal Component Regression) and PLSR calibration methods, incorporating a PCA-based outlier detection strategy to enhance prediction model stability. The study by [35] centers on predicting N, P, and K. When juxtaposed with [33], and [35] evaluates performance variations across diverse spectral regions, encompassing NIR, MIR, and a combination of both. Interestingly, the preprocessing in [35] omits the water removal step, which was present in [33]. This suggests that their calibration model might exhibit increased resilience to fluctuations in soil moisture levels. By employing Least Squares Support Vector Regressions (LS-SVM), they achieved superior results compared to the conventional PLSR method within the NIR or MIR spectrum. This study underscores the efficacy of LS-SVM in precisely gauging soil attributes via infrared reflectance spectroscopy. On another front, [PERSON]. [34] present a novel perspective on soil sensing aimed at macronutrients. Instead of traditional spectrometers, they employ LEDs and sensors embedded on a circuit board. However, there are certain limitations to their approach. For one, even when taking into account the LEDs' lower resolution compared to traditional spectrometers, the target element concentration level span (0 to 50%) seems considerably elevated relative to prior research. Additionally, instead of leveraging conventional calibration models such as PLSR, LS-SVR, or neural networks, [PERSON]. [34] utilize a unique curve for their predictive model. This approach might constrain the model's generalizability. Notwithstanding these concerns, their research ushers in an innovative perspective on reflectance spectroscopy by harnessing LEDs and sensors, marking significant progress towards real-world, on-location deployments versus controlled lab-based experiments with spectrometers. The study by [60] showed that, without the presence of coarse crumb, PLSR could accurately determine soil attributes such as Total Nitrogen (TN), Total Phosphorus (TP), and Total Potassium (TK) in a laboratory setting using vis-NIR reflectance spectra. This research provided a rapid technique for soil classification aligned with the Chinese Soil Taxonomy (CST) by tapping into properties linked to CST. Regarding individual macronutrient detection, [PERSON]. [61] formulated a hyperspectral model, emphasizing Soil Nitrogen (SN) estimation, grounded on the PLSR calibration method. Samples were sourced from diverse agricultural lands in Maharashtra, India, enhancing the model's versatility. The research identified several spectral bands sensitive to soil nitrogen content, including 480 nm, 511 nm, 653 nm, 997 nm, 1472 nm, 1795 nm, 2210 nm, and 2296 nm. Certain studies deeply analyze nitrogen detection, intertwining this process with other crucial elements like TC [62]. Specifically, [PERSON]. [62] crafted a methodology for swift on-site evaluations of C and N using a portable spectroradiometer, the ASD FieldSpecPro. The study highlighted that the accuracy of PLSR-based calibration improves when the training datasets align spectrally with the target datasets. In related work, [PERSON]. [63] indicated that training and testing phosphorus detection on soil samples from diverse fields amplifies the complexity, making practical applications more challenging. The research by [64] underscored that the prediction of total phosphorus is linked to SC detection in the NIR spectrum. Moreover, [PERSON].