# Survey for Soil Sensing with IOT and Traditional Systems
## 1 Introduction  
Smart agriculture has increasingly become a focal point of interest. Implementing a smart agriculture system can assist farmers in achieving precision irrigation [1; 2; 3], leading to more efficient water usage. Additionally, the integration of such systems can aid farmers in monitoring their soil's fertility levels. This not only boosts their crop yield [4], but also helps in averting over-fertilization, which can contaminate the soil and groundwater [5; 6].  
Soil sample measurements using reflectance spectroscopy are regarded as reliable due to their high repeatability and reproducibility [7]. These methods are also viewed as swift and cost-effective tools for soil characterization [8; 9]. Traditional reflectance spectroscopy is typically conducted in laboratory settings using spectrometers [10], accompanied by several preprocessing steps. These preprocessing activities aim to minimize the effects of soil moisture and particle size, thereby reducing covariables in soil property predictions [11]. However, the preprocessing requirements further confine the experiment environment to the laboratory. A gap remains between laboratory experiments and real-world, on-site detection.  
Recently, RF-based soil sensing has been introduced as a portable and cost-effective alternative for on-site soil analysis [12; 13; 14; 15; 16]. When contrasted with traditional reflectance spectroscopy experiments, RF-based techniques present several advantages. First, the equipment used, such as RFID tags of WiFi device is more widely available and affordable [12; 13; 14]. Second, RF-based soil sensing eliminates the need for preprocessing steps, bridging the gap between laboratory and real-world applications. Moreover, thanks to the property of RF signals, these methods can encompass a broader area compared to traditional techniques.  
Given the burgeoning interest in soil property sensing, numerous studies have been undertaken to encapsulate the advancements in this domain [7; 17; 18; 19; 20; 21; 22]. Nevertheless,the recent advent of RF-based soil property sensing has brought to the fore IoT-based systems showcasing the potential for affordable and mobile soil sensing solutions. These emerging contributions have not been comprehensively reviewed and encapsulated. Table 1 delineates the distinctions between the topics covered in literature reviews of soil sensing methodologies over the past two decades and the content of our research.  
In this study, we undertake a comprehensive review of soil property sensing methodologies, encompassing both traditional reflectance spectroscopy-based systems and RF-based IoT soil sensing systems. In addition to this, we delve into an in-depth discourse on soil sample preparation procedures and spectral information analysis methods. We also compare the merits and limitations of traditional and IoT methods, and contemplate potential directions for future soil property sensing research.  
The organization of this paper is as follows: In the Section 1, we introduce the background of soil sensing. In Section 2, we present fundamental concepts related to soil reflectance spectroscopy and analyze traditional soil sensing methods based on reflectance spectroscopy, with a particular focus on key elements such as soil carbon, soil moisture, and soil macronutrients. The Section 3 outlines state-of-the-art RF-based IoT soil sensing systems. In Section 4, we detail various inference methods and evaluation metrics. In Section 5, we explore future prospects of soil property sensing, considering the strengths and weaknesses of existing approaches. Finally, we provide Section 6 to encapsulate the key points of this paper.