Comprehensive Evaluation of Soil Moisture Sensing Technology Applications Based on Analytic Hierarchy Process and Delphi

The demand for smart irrigation and water-saving practices in agriculture has triggered the development of different soil moisture sensing techniques that can operate under harsh field conditions. In this study, a soil moisture sensing technology appropriate for the field applications was comprehensively evaluated. From a qualitative and quantitative perspective, the Delphi and analytic hierarchy process methods were used to construct an index system involving technological advantage, economic benefit, risk analysis, policy support, four second-level indicators, and 23 fourthlevel indicators. The results showed that economic benefits account for the largest weight. The practical evaluation resulted in 12 farms that showed that the selected soil water sensing methods performed reasonably and exhibited obvious water-saving irrigation benefits, which are usually used for scheduling irrigation. The overall score of M4 in different soil types was 0.2% lower than that of M5. Farms with reasonable economic conditions and a high awareness scored 5.3% higher on technology than those with modest economic conditions, which clearly affects the evaluation scores of the two technologies. The evaluation results help farmers and government decision-making bodies in technology selection, production decision-making, and risk control.


Introduction
The demand for smart irrigation and water-saving practices in agriculture has triggered the development of different soil moisture sensing techniques that can operate under harsh field conditions. Continuous monitoring of soil moisture is a common practice in digital agriculture to minimize the effects of waterlogging and drought, and increase yield and profitability. Soil moisture measurement methods include the gravity [1], tension [2], neutron [3], γ-ray projection [4], dielectric [5], remote sensing [6], and optical [7] methods. Indeed, each method has its own characteristics and application scenarios. The effect of each method can also be completely different from that of the other methods depending on the soil type, temperature, and salinity [5]. Many studies have investigated the performance of soil water sensing technologies [8]; this is a continuous process as the performance of sensors is constantly improving [9]. Several sensors were tested in different soils to evaluate the measurement accuracy of each sensor in different soils. The exiting studies have primarily focused on the precision and performance improvements in certain

Overview of Soil Moisture Sensor Technology
The measurement of soil moisture in the field is currently based on sensing technologies, such as dielectric [5], remote [16], and thermal [17] sensing, all of which employ different technical methods; their measurement principles and technical characteristics are shown in Table 1. In this study, the technical characteristics of the technology were fully examined considering their measurement principles to quantify the evaluation indices. As the capacitive sensors have been extensively applied in digital agriculture, two commercially available probes, ECH2O EC-5 and ECHO 10HS (Decagon Devices Inc., Pullman, WA, USA) interfaced with a WiFi datalogger (Adaptive AgroTech, Potsdam, Germany) for excitation are shown in Figure 1. Both probes incorporate high-frequency oscillations to deliver accurate results and determine volumetric water content by measuring the charge time of a capacitor using the soil as a dielectric (i.e., utilization of the capacitance/frequency Agriculture 2021, 11,1116 3 of 16 domain technology) and generating an analog output. The dimensions (length, width, thickness) for ECH2O EC-5 are 8.9, 1.8, 0.7 cm, and for ECHO 10HS are 16, 3.3, and 0.8 cm, making them appropriate candidates for different field applications. The AgroTech WiFi datalogger has been optimized for operation under harsh field conditions; thus, it was capable of delivering an excitation signal of 3.6 V to the sensor probes at a user-defined interval (typically every 10 min), log the analog output on an onboard SD card, and upload the measurement results to a private cloud storage via a WiFi connection. Moreover, the sampling volume of each sensor probe and calibration equations are defined by the manufacturer or determined using laboratory tests; thus they may change depending on the datalogger employed.

Technical Name Measuring Principle Technical Characteristics
Dielectric method [5] The soil dielectric constant is indirectly measured to determine soil moisture content.

Technical Name Measuring Principle Technical Characteristics
Resistance method (M5) This method measures the resistance between electrodes inserted into a medium. Humidity is determined, and then a calibration curve is developed using voltage-water ratios to estimate the soil water content.
Advantages: low cost, anti-corruption, adjustable point embedding, and automatic monitoring. Disadvantages: destroys soil structure, making it vulnerable to the influence of the soil temperature, salinity, and soil texture for calibration purposes.
Remote sensing method [16] The soil surface is monitored using remote sensor measurements of reflected or emitted electromagnetic energy, which is analyzed to establish the relationship between the soil moisture and soil water content.
Advantages: all-day, all-weather, and multi-polar applications.
Hyperspectral remote-sensing method (M6) Hyperspectral remote sensing technology can directly establish a relationship between the soil moisture and soil reflectance to monitor the soil moisture.
Advantages: high spectral resolution within a specific band (range).
Microwave remote sensing method (M7) The microwave remote sensing method uses the soil moisture contrast in the dielectric constant to invert the soil moisture.
Advantages: all-day, all-weather, multi-polar applications, high resolution, penetrability, sensitivity to the soil moisture content. Disadvantages: The vegetation and soil moisture monitoring precision are low.
Heat pulse sensing method [17] Heat pulse sensing method (M8) The heat pulse method uses the linear relationship between soil moisture content and volumetric heat capacity.
Advantages: accuracy, less influenced by salinity.  Figure 1. Both probes incorporate high-frequency oscillations to deliver accurate results and determine volumetric water content by measuring the charge time of a capacitor using the soil as a dielectric (i.e., utilization of the capacitance/frequency domain technology) and generating an analog output. The dimensions (length, width, thickness) for ECH2O EC-5 are 8.9, 1.8, 0.7 cm, and for ECHO 10HS are 16, 3.3, and 0.8 cm, making them appropriate candidates for different field applications. The AgroTech WiFi datalogger has been optimized for operation under harsh field conditions; thus, it was capable of delivering an excitation signal of 3.6 V to the sensor probes at a user-defined interval (typically every 10 min), log the analog output on an onboard SD card, and upload the measurement results to a private cloud storage via a WiFi connection. Moreover, the sampling volume of each sensor probe and calibration equations are defined by the manufacturer or determined using laboratory tests; thus they may change depending on the datalogger employed. Internet of Things (IoT) monitoring of soil moisture data using two capacitive soil moisture probes interfaced with a WiFi datalogger that has been custom-built to withstand harsh field conditions.  Internet of Things (IoT) monitoring of soil moisture data using two capacitive soil moisture probes interfaced with a WiFi datalogger that has been custom-built to withstand harsh field conditions. The index system [18] is the foundation for evaluation, directly affecting the reliability and validity of the evaluation results. A mature evaluation index system should be scientific, systematic, concise, comparable, and operable. In addition, it should be complete, comprehensive, and fully reflect the applicability of the soil water sensing technology. All indicators should be concise and simultaneously reflect their relationship with the objects. The index system structure should be rigid, and the definition of indices be clear. Moreover, an index should provide accurate comparative information to be used as an evaluation index system. Finally, all metrics should be actionable.
The applicability engineering theory, economic principles, and system analysis methods are used to prove that the selection principle of a single index should be based on the following eight criteria [19], which are also used in ecology. That is, it is measurable (M) [20], sensitive (V) [21], predictable (P) [19], typical (T), controllable (C) [19], integrative (I), responsible (R), and stable (S). Among these criteria, I indicates the construction of the entire index system, whereas the remaining seven criteria are requirements for the selection of each index [22].

Influencing Factors of Comprehensive Evaluation
The influencing factors of a comprehensive technology evaluation [23] include the technology itself, economic benefit, risk analysis, and policy support. A technology is applied primarily to reduce production inputs and increase outputs, while its applicability, advancement, and application capability are key characteristics. Sustainable policy support is an important motive force in the application of a technology. The economic benefit is the index measurement of the technology inputs and outputs. The application of technologies is often risky, and thus the application characteristics of the field should be fully considered when determining certain indices. The field environment includes natural and artificial environments. The natural environment is inherited from the natural ecosystem; however, it is regulated and controlled by humans to varying degrees. Features of natural environments include the temperature, light, and physical and chemical properties of the soil in the crop population. The artificial environment refers to the input of various social resources to farmland, such as intelligent irrigation, pest control, and online soil moisture testing. Furthermore, field environmental information technology has been widely promoted, in which field soil moisture measurement devices are used to obtain real-time and stable soil moisture data to ultimately realize the online detection of soil moisture for crop growth. In general, the equipment is buried in soil to obtain the soil moisture content at different depths and fulfill different requirements.

Construction Method of Evaluation Index System
According to the characteristics of the soil water sensing technology, influencing factors, and the selection principles of evaluation indices, the selection of evaluation factors should not only meet the technical evaluation strategic standards at a macro-industry level but also reflect the requirements of micro users at the project and operational level, effectively integrating the evaluation index system.
Forty-six articles were found by searching the keywords "soil water sensing technology," "technology evaluation," and "comprehensive evaluation." The framework of the proposed comprehensive technology evaluation index system was constructed considering the results presented in the existing studies. Consequently, the Delphi method was employed to screen and optimize evaluation indices of the proposed soil water sensing technology, as shown in Figure 2.
The presented results assist technology exporters who reference academic studies, agricultural IoT companies, and technology users who mainly reference agricultural business entities, family farms, farmers cooperatives, corporate farms, leading planting enterprises, and high-level managers who reference the relevant government departments. Various stakeholders in Daejeon use the environmental information technology evaluation for different purposes. Consequently, certain stakeholders including six IoT information technology specialists (P1) from scientific research institutions, 20 cooperative heads (P2) involved in agricultural cultivation, 20 people in the field of farming technology (P3), six agricultural IoT technology suppliers (P4), and six government technical personnel from the industry sector (P5) were selected to participate in the survey. When over 70% of the personnel of a certain category considered that an indicator should be selected, the personnel of this category were deemed to have approved that indicator. An indicator was selected when the experts of three or more categories had approved it.
Agriculture 2021, 11, x FOR PEER REVIEW 6 the personnel of this category were deemed to have approved that indicator. An indi was selected when the experts of three or more categories had approved it. Index optimization and quantification were completed considering the basic i system. Experts measured each indicator based on the seven principles for its inclu which were developed in the construction principles of the evaluation index system indicator was retained when it conformed to five or more principles. The degree of re nition and standards appropriate for the stakeholders revealed that many indicators c not meet the important criteria, which were being measurable (M), sensitive (V), ty (T), controllable (C), responsible (R), or impossible to quantify. Indicators that did not with the soil moisture sensing technology, or incorporated the content of overlappin dicators, were deleted. After determining the evaluation index system, indices were q tified to score the standard.

Weight Calculation Method of Index System
The AHP [24] and Delphi [25] methods were used to determine the weights of indicator. The minimum number of appropriate experts is seven to eight [26], with than 10 experts being able to obtain a moderately accurate outcome. Therefore, w signed questionnaires for 10 experts from the categories P1-5, who had at least 10 y of experience in the use of agricultural IoT. The weight analysis of each index was formed based on the nine-degree relative importance score of the AHP method. The tive importance evaluation was divided into nine grades (1-9 points), while the com hensive evaluation results were divided into five grades. The evaluation process w follows: the indices are compared pair by pair, and the comparison results are written in a m form based on the AHP method constructed using the judgment matrix, as show Equation (1), where ui is the evaluation index ( , and uij is the rel importance of ui to uj (j = 1, 2, …, m). The value is expressed on a scale in the range o where 9 indicates an increased importance and 1 indicates the equality. The highe score, the higher is the importance. When uji = 1/ uij, then we can write, Index optimization and quantification were completed considering the basic index system. Experts measured each indicator based on the seven principles for its inclusion, which were developed in the construction principles of the evaluation index system. An indicator was retained when it conformed to five or more principles. The degree of recognition and standards appropriate for the stakeholders revealed that many indicators could not meet the important criteria, which were being measurable (M), sensitive (V), typical (T), controllable (C), responsible (R), or impossible to quantify. Indicators that did not deal with the soil moisture sensing technology, or incorporated the content of overlapping indicators, were deleted. After determining the evaluation index system, indices were quantified to score the standard.

Weight Calculation Method of Index System
The AHP [24] and Delphi [25] methods were used to determine the weights of each indicator. The minimum number of appropriate experts is seven to eight [26], with more than 10 experts being able to obtain a moderately accurate outcome. Therefore, we designed questionnaires for 10 experts from the categories P1-5, who had at least 10 years of experience in the use of agricultural IoT. The weight analysis of each index was performed based on the nine-degree relative importance score of the AHP method. The relative importance evaluation was divided into nine grades (1-9 points), while the comprehensive evaluation results were divided into five grades. The evaluation process was as follows: The indices are compared pair by pair, and the comparison results are written in a matrix form based on the AHP method constructed using the judgment matrix, as shown in Equation (1), where u i is the evaluation index (u i ∈ U(i = 1, 2, · · · n)), and u ij is the relative importance of u i to u j (j = 1, 2, . . . , m). The value is expressed on a scale in the range of 1-9, where 9 indicates an increased importance and 1 indicates the equality. The higher the score, the higher is the importance. When u ji = 1/ u ij , then we can write, The judgment matrix is employed to calculate the weight of factor i (W i 1 ) using Equations (2)-(6), where m is the number of factors in row i or column j of the matrix, λ max is the maximum eigenvalue of the matrix, W i is the weight of factor i in the comprehensive evaluation index level, and W i 1 is the final weight of factor i at the entire level, which can be calculated as the product of the weight of each factor and that of the corresponding element.
The judgment matrix must be evaluated for consistency because of the subjectivity of the AHP method, as expressed in Equation (7). A random consistency ratio (CR) of less than 0.1 indicates a satisfactory consistency; otherwise, the matrix must be adjusted. CI is an indicator of the judgment matrix consistency, as expressed in Equation (8). This is the same when CI = 0; however, when CI = 0, the results are not consistent.

Grade of Comprehensive Evaluation Results
In particular, levels 3-5 are appropriate for technological classification. After expert consultation, the evaluation results of the soil moisture sensing technology in the field were divided into five levels together with evaluation indexes, calculation methods, and classification standards, as shown in Table 2.

Empirical Program Design
In this study, the performance of the capacitance (M4) and resistance (M5) soil moisture sensing methods, which are typically used in the market, were evaluated in different areas. Family farms are typical large-scale farmers in China, which mainly employ soil moisture sensing technologies in agriculture. Therefore, a family farm was chosen as the scoring subject in this study. Six types of soil in Shandong Province include brown, cinnamon, tidal, sandy ginger black, saline-alkali, and paddy soils. For instance, paddy soil is mainly planted with rice, and saline-alkali soil is planted with sunflower and wolfberry. To eliminate the differences in planting crops, we selected three farms in each of brown, cinnamon, tidal, and sandy ginger black soils that could all grow corn. The specific conditions of each farm are listed in Table 3.  Qingdao pingdu 160 square yards, corn and wheat rotation base, 10 years, reasonable, strong.

Determination of Evaluation Index System
After collecting and screening the soil water sensing technologies, including technical and comprehensive evaluations, the second, third, and fourth indices of the basic index system were selected, as shown in Table 4. Sixty-two questionnaires on a technical basic index system were distributed to the participants, sixty-two were retrieved with a recovery Agriculture 2021, 11, 1116 9 of 16 rate of 100%, and 10 were interviewed onsite. The survey and interview results are listed in Table 4. Based on the results shown in Table 4, the indices were selected and optimized for the comprehensive evaluation of soil water sensing technology in the field. C6 is changed to the soil type and shall apply C6*, C7 to apply the ambient temperature C7*, C8 to the existing facilities ability C8*, C9 to operation complexity C9*, and C10 to operational dependence C10*. B4 is the renamed use efficiency B4*. C11 is changed to an increasing efficiency C11*, C12 to save labor C12*, C13 to water-saving irrigation benefit C13*. B8, B9, B12, C19, C20, C23, C24, and C25 are deleted. Five new indices are named technology to implement supply chain stability C17-1, technological environment stability using C17-2, further development to disable risk technology C17-3, technical implementation of the natural environment risk C18-1, and technology implementation of artificial environment risk C18-2. Moreover, codes are used instead of index names. Finally, 23 level-4 indicators were selected. The characteristics of the indicators and scoring instructions were determined using the technical characteristics listed in Table 1. The results are listed in Table 5.

Weight Results of Comprehensive Evaluation
Ten questionnaires were disseminated and collected, with a recovery rate of 100%. Figure 3 presents the index weight results after applying the AHP method, where G is the overall target with a weight of 1.0000.

Three-Level Index
Four-Level Index Index Score Shows that All Indexes Are Set to 0 to 1 Grade

Weight Results of Comprehensive Evaluation
Ten questionnaires were disseminated and collected, with a recovery rate of 100%. Figure 3 presents the index weight results after applying the AHP method, where G is the overall target with a weight of 1.0000.

Comprehensive Evaluation Influence Weight Analysis
The calculated scoring weights of 10 experts revealed that the proportion of economic benefits in the entire index system was 0.44351, accounting for approximately half of the total; thus, the ultimate purpose of the proposed technology is to improve the economic benefits. The weight distributions of the second, third, and fourth indices are shown in Figure 4.

Comprehensive Evaluation Influence Weight Analysis
The calculated scoring weights of 10 experts revealed that the proportion of economic benefits in the entire index system was 0.44351, accounting for approximately half of the total; thus, the ultimate purpose of the proposed technology is to improve the economic benefits. The weight distributions of the second, third, and fourth indices are shown in Figure 4.  The applicability of the proposed technology is even more important. Applicability refers to the extent to which a product meets the needs of users. In part 11 of the standard ISO9241 (Guide on Usability, 1998), applicability is defined as follows: "the effectiveness, efficiency, and satisfaction of a given user using a product to achieve a given goal in a given context." The applicability of a technology is an important factor in the application and promotion of mature technological industries [27].
As for the economic benefits, an increase in the yield and efficiency accounted for the largest proportion of all indicators. The evaluation results showed that the proportion of water-saving irrigation benefits was 0.148, which was the largest among all the indicators. Moreover, the use of information technology is intended to increase the efficiency of agricultural production and output, and simultaneously reduce inputs. Many existing studies have shown that dynamic irrigation yields obvious gains compared to uniform irrigation [28], and improper irrigation time for cotton can lead to a yield loss [29]. Dioudis conducted experiments for 2 years and proved that the use of TDR sensors to monitor the soil moisture and realize water-saving irrigation could significantly reduce management costs, including irrigation water, manpower, energy, etc. [30], and the sensor-based method was an excellent irrigation scheduling strategy [31]. Moreover, the proportion of water-saving irrigation evaluation in this study is consistent with those presented in the existing studies on the benefits of water-saving.
As for the risk analysis, the risks of implementation outweigh those of the technology itself. The soil moisture sensing technology is a conventional technology with an excellent maturity and stability. Consequently, the ability of the operator and the risk of the application environment in the implementation can significantly affect the efficiency of the application. As for the policy management, the state subsidy policy is also a factor in the adoption of technologies for agricultural operators.

Comprehensive Results in One Farm
The comprehensive scores of the capacitance (M4) and resistance (M5) soil water The applicability of the proposed technology is even more important. Applicability refers to the extent to which a product meets the needs of users. In part 11 of the standard ISO9241 (Guide on Usability, 1998), applicability is defined as follows: "the effectiveness, efficiency, and satisfaction of a given user using a product to achieve a given goal in a given context." The applicability of a technology is an important factor in the application and promotion of mature technological industries [27].
As for the economic benefits, an increase in the yield and efficiency accounted for the largest proportion of all indicators. The evaluation results showed that the proportion of water-saving irrigation benefits was 0.148, which was the largest among all the indicators. Moreover, the use of information technology is intended to increase the efficiency of agricultural production and output, and simultaneously reduce inputs. Many existing studies have shown that dynamic irrigation yields obvious gains compared to uniform irrigation [28], and improper irrigation time for cotton can lead to a yield loss [29]. Dioudis conducted experiments for 2 years and proved that the use of TDR sensors to monitor the soil moisture and realize water-saving irrigation could significantly reduce management costs, including irrigation water, manpower, energy, etc. [30], and the sensor-based method was an excellent irrigation scheduling strategy [31]. Moreover, the proportion of water-saving irrigation evaluation in this study is consistent with those presented in the existing studies on the benefits of water-saving.
As for the risk analysis, the risks of implementation outweigh those of the technology itself. The soil moisture sensing technology is a conventional technology with an excellent maturity and stability. Consequently, the ability of the operator and the risk of the application environment in the implementation can significantly affect the efficiency of the application. As for the policy management, the state subsidy policy is also a factor in the adoption of technologies for agricultural operators.

Comprehensive Results in One Farm
The comprehensive scores of the capacitance (M4) and resistance (M5) soil water sensing methods are shown in Table 6. As shown in Table 6, the comprehensive evaluation results of the soil water sensing methods in corn planting were 0.73991 and 0.73836, respectively. Thus, M4 performed slightly better than M5. Both methods are mature with comparable market prices. In the corn planting scenario, M4 exhibits a better effect on the soil profile water monitoring considering its technical characteristics [5], which is conducive to monitoring the water changes near the deep roots of corn. The multi-depth real-time system introduced by Sui et al. for monitoring corn, soybean, and cotton using capacitive sensors could guide irrigation scheduling [32], indicating that soil moisture sensors could effectively guide water-saving irrigation and obtain economic benefits, which is consistent with the evaluation results of this study.

Comprehensive Results in All Farms
Results of evaluation of 12 farms are below figure. Figure 5 illustrates that the evaluation results of M4 and M5 were both above 0.7, indicating that the farmers recognized the performance of the two methods. In gray-ginger black soil, M5 is significantly superior to M4 because M4 is more sensitive to the physical properties of the gray-ginger black soil [33]. In the brown, cinnamon, and tidal soils, a small difference exists between them, which is determined by the characteristics of the two methods [34,35].  Figure 6 shows that the score of the two methods in unfortunate economic conditions is the lowest (0.702) for the farmers with a poor awareness of information technology (Heze Yuncheng), whereas the highest score (0.755) is achieved by the Weifang Anqiu region. Therefore, the score of farms with acceptable economic conditions and farmers′ awareness of information technology is 5.3% higher than that of farms with poor economic conditions. In addition, Linyi Linshu is a region with poor economic conditions that gains a score value of 0.717. The score values for the regions with moderate economic conditions and farmers′ information technology awareness are in the range of 0.724-0.731, while those of the regions with acceptable economic conditions and farmers′ information technology awareness range from 0.740 to 0.751. Therefore, the choice of the two methods largely depends on the regional economic conditions and farmers′ awareness of information technology [36][37][38][39].

Conclusions
In this study, a comprehensive method was introduced to evaluate the performance of a soil moisture sensing technology applied to a field environment. First, the principles, methods, and influencing factors of the soil water sensing technology were examined and   Figure 6 shows that the score of the two methods in unfortunate economic conditions is the lowest (0.702) for the farmers with a poor awareness of information technology (Heze Yuncheng), whereas the highest score (0.755) is achieved by the Weifang Anqiu region. Therefore, the score of farms with acceptable economic conditions and farmers' awareness of information technology is 5.3% higher than that of farms with poor economic conditions. In addition, Linyi Linshu is a region with poor economic conditions that gains a score value of 0.717. The score values for the regions with moderate economic conditions and farmers' information technology awareness are in the range of 0.724-0.731, while those of the regions with acceptable economic conditions and farmers' information technology awareness range from 0.740 to 0.751. Therefore, the choice of the two methods largely depends on the regional economic conditions and farmers' awareness of information technology [36][37][38][39].  Figure 6 shows that the score of the two methods in unfortunate economic conditions is the lowest (0.702) for the farmers with a poor awareness of information technology (Heze Yuncheng), whereas the highest score (0.755) is achieved by the Weifang Anqiu region. Therefore, the score of farms with acceptable economic conditions and farmers′ awareness of information technology is 5.3% higher than that of farms with poor economic conditions. In addition, Linyi Linshu is a region with poor economic conditions that gains a score value of 0.717. The score values for the regions with moderate economic conditions and farmers′ information technology awareness are in the range of 0.724-0.731, while those of the regions with acceptable economic conditions and farmers′ information technology awareness range from 0.740 to 0.751. Therefore, the choice of the two methods largely depends on the regional economic conditions and farmers′ awareness of information technology [36][37][38][39].

Conclusions
In this study, a comprehensive method was introduced to evaluate the performance of a soil moisture sensing technology applied to a field environment. First, the principles, methods, and influencing factors of the soil water sensing technology were examined and

Conclusions
In this study, a comprehensive method was introduced to evaluate the performance of a soil moisture sensing technology applied to a field environment. First, the principles, methods, and influencing factors of the soil water sensing technology were examined and analyzed, and an evaluation index system was preliminarily established. The Delphi method was used to qualitatively and quantitatively screen the proposed evaluation index system. The irrelevant and similar indices were removed and combined, respectively. Finally, four second-level indices and 23 fourth-level indices were retained, and the AHP and Delphi methods were used to construct index weights. A comprehensive evaluation model of the soil moisture sensing technology was used in 12 farms in Shandong Province to apply the comprehensive high-standard field environmental information technology. The results showed that M4 (average score = 0.734) based on the capacitance method was slightly worse than M5 (average score = 0.736) based on the resistance method, both of which were above the medium level. However, M5 outperformed M4 in the sand ginger black soil. Therefore, the method is generally applicable and can provide certain economic benefits. Simultaneously, economic conditions and farmers' awareness of information technology significantly affect the evaluation scores of the two methods. The score of farms with acceptable economic conditions and farmers' awareness of information technology is 5.3% higher than that of farms with poor economic conditions. The results provide agricultural operators with a guidance and suggestions in terms of the selection of an appropriate soil moisture sensing technology. The next study will be conducted to examine the differences in the application of information technology in different fields, optimize the evaluation index system, and evaluate other types of field information technologies, such as weather stations, UAVs, pest monitoring, and intelligent irrigation.