CHARACTERIZATION OF LOW-COST CAPACITIVE SOIL MOISTURE SENSORS FOR IOT NETWORKS - MDPI
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sensors Article Characterization of Low-Cost Capacitive Soil Moisture Sensors for IoT Networks Pisana Placidi * , Laura Gasperini, Alessandro Grassi, Manuela Cecconi and Andrea Scorzoni Dipartimento di Ingegneria, University of Perugia, via G. Duranti, 93, 06125 Perugia, Italy; laura.gasperini2@studenti.unipg.it (L.G.); alessandro.grassi@studenti.unipg.it (A.G.); manuela.cecconi@unipg.it (M.C.); andrea.scorzoni@unipg.it (A.S.) * Correspondence: pisana.placidi@unipg.it Received: 31 May 2020; Accepted: 23 June 2020; Published: 25 June 2020 Abstract: The rapid development and wide application of the IoT (Internet of Things) has pushed toward the improvement of current practices in greenhouse technology and agriculture in general, through automation and informatization. The experimental and accurate determination of soil moisture is a matter of great importance in different scientific fields, such as agronomy, soil physics, geology, hydraulics, and soil mechanics. This paper focuses on the experimental characterization of a commercial low-cost “capacitive” coplanar soil moisture sensor that can be housed in distributed nodes for IoT applications. It is shown that at least for a well-defined type of soil with a constant solid matter to volume ratio, this type of capacitive sensor yields a reliable relationship between output voltage and gravimetric water content. Keywords: moisture sensor; capacitive measurement; capacitive moisture sensor; soil water content measurement techniques; geotechnical investigation; SKU:SEN0193 sensor 1. Introduction The development of the Internet of Things (IoT) refers to a global network of intelligent objects, or “things,” based on sensors, i.e., microcontrollers augmented with networking capabilities. In this framework, communication technologies can improve the current methods of monitoring, supporting the response appropriately in real time for a wide range of applications [1–4]. Sensors are designed for collecting information (e.g., temperature, pressure, light, humidity, soil moisture, etc.) whereas network-capable microcontrollers are able to process, store, and interpret information, building intelligent wireless sensor networks (WSN) [5–7]. WSNs have extensively been adopted in agriculture since their first introduction in the new century [8,9]. A distinct advantage of wireless transmission is a significant reduction and simplification in wiring and harness, a required feature for smart farming [10,11], where installation flexibility for sensors is not an option. A description of a modular IoT architecture for several applications including but not limited to healthcare, health monitoring, and precision agriculture is reported in previous works [12,13]. In this scenario, soil moisture or soil water content is a matter of great importance. In fact, water is considered as one of the most critical resources for sustainable development because in forthcoming years fresh water will increasingly be used in irrigated areas, in towns for domestic purposes, and in the industry. Furthermore, the efficiency of irrigation is very low. It is well known that only a fraction of the irrigation water is actually used by the crops. Hence, the sustainable use of irrigation water is a main concern for agriculture and not only under scarcity conditions. Considerable efforts have been allocated over time to increase water efficiency based on the assertion that “more can be achieved with less water through better management” [14]. Sensors 2020, 20, 3585; doi:10.3390/s20123585 www.mdpi.com/journal/sensors
Sensors 2020, 20, 3585 2 of 14 Modernization and automated scheduling of irrigation systems demands for sensor-based equipment. Traditionally, sensor data are associated with environmental conditions and soil water status to provide information about the full crop water requirements [15]. The most commonly used soil parameter sensors exploit dielectric properties, since they are relatively cheap and flexible [16,17]. Their correct operation requires complex calibration, taking into account aspects such as soil texture and structure, temperature, and water salinity [10,17–19], as well as the spatial variability of soil conditions [20]. Thermal and multispectral cameras, satellites, or infrared radiometers (IR) are also used to estimate water crop needs [21–24]. Nonetheless, the experimental and accurate determination of soil moisture is also a matter of great importance in different scientific fields, such as agronomy, soil physics, geology, hydraulics, and soil mechanics. Physical, chemical, mineralogical, and biological properties are also affected by the soil water content. A summary of the state of the art soil moisture measurement techniques has been previously reported [24–28]. The thermo-gravimetric technique is the classical soil moisture (or water content) measurement method for geotechnical engineering applications. Modern techniques include soil resistivity detection, neutron scattering, tensiometers, infrared moisture balance, dielectric techniques like frequency domain reflectometry, time domain reflectometry, heat flux soil moisture sensors, optical techniques, and modern micro-electromechanical systems. In particular, several reviews have been published in the literature on dielectric methods [29–31]. Our paper focuses on the experimental characterization of a commercial, low-cost “capacitive” soil moisture sensor that can be housed in distributed nodes for IoT applications with the aim to validate its performance and soundness in the determination of soil physical properties. IoT sensor nodes should be deployed in large numbers and the cost of the components of the nodes should be minimized. The chosen sensor, identified as SKU:SEN0193, is the cheapest and most easily available in the market. However, detailed information on the sensor operation is not available. Therefore, it is useful to investigate its performance and understand its limits both for irrigation management and for soil moisture determination, e.g., in geotechnical applications. To the best of our knowledge, the sensor studied in the present paper has been characterized only once before [32], when it as evaluated for accuracy and reliability under laboratory conditions. The authors found that this sensor did not perform acceptably in predicting soil moisture content in a laboratory soil mixture prepared by mixing organic-rich soil and vermiculite, while it can estimate soil water in gardening soil in the so-called “field capacity” range. The present paper adds an in-depth characterization of the electrical circuit of the sensor and a statistical analysis on a number of nominally identical samples with the aim to better understand its limits and applicability with silica sandy soil. 2. Soil Water Content Measurements Undoubtedly, water plays an essential role in the chemical-physical-mechanical properties of soil. With regard to the upper soil volumes close to the ground table, plant growth, organization of natural ecosystems, and biodiversity, are surely affected by soil moisture quantity and its variations. In the agriculture sector, application of adequate and timely irrigation, depending upon the soil-moisture-plant environment, is essential for crop production. More generally, soil vegetation is certainly affected by soil moisture, among others. Therefore, quantitative evaluation of soil water content from the ground surface to larger depths is a key-issue for the comprehension and assessment of many phenomena depending on the interaction soil-vegetation-atmosphere, such as soil erosion, runoff, and soil water infiltration. The subject is complex since the study of these processes requires specific skills in the field of soil physics, agronomy, hydraulics, and soil mechanics. Soil vegetation modifies the hydrological balance of the involved area due to both the aerial plant apparati to capture part of the water rainfall and the capacity of the plants to adsorb water from the surrounding soil and transfer it to the atmosphere through evapotranspiration. The latter mechanism may yield a reduction of the soil degree of saturation (an increase of suction) and consequently an increase of the soil shear strength.
Sensors 2020, 20, 3585 3 of 14 Sensors 2020, 20, x FOR PEER REVIEW Thus, soil vegetation may also have an important effect in the engineering field of slope stability3and of 14 environmental protection [33,34]. Hence, the determination of soil water content, degree of saturation, and their variations upon Hence, the determination of soil water content, degree of saturation, and their variations upon environmental conditions is crucial in the different fields dealing with the soil behavior. Pore environmental conditions is crucial in the different fields dealing with the soil behavior. Pore pressures pressures pertaining to the soil fluid phase (gas + water) also play a fundamental role in the pertaining to the soil fluid phase (gas + water) also play a fundamental role in the mechanical behavior mechanical behavior of soils. In turn, the latter impacts the performance of geotechnical structures of soils. In turn, the latter impacts the performance of geotechnical structures and systems, such as and systems, such as foundations, earth retaining walls, slopes, and so on. foundations, earth retaining walls, slopes, and so on. Looking at the scale of a soil element, by being a porous material, this is intrinsically multiphase. Looking at the scale of a soil element, by being a porous material, this is intrinsically multiphase. Typically, three distinct phases are recognized: solid (mineral particles), liquid (usually water), and Typically, three distinct phases are recognized: solid (mineral particles), liquid (usually water), gas. In the framework of soil mechanics, the relationships among the soil phases are schematically and gas. In the framework of soil mechanics, the relationships among the soil phases are schematically represented in Figure 1, which facilitates the definition of the phase relationships. Although the represented in Figure 1, which facilitates the definition of the phase relationships. Although the quantities plotted in the figure and their relationships are very well established in the fields of soil quantities plotted in the figure and their relationships are very well established in the fields of soil mechanics, for the sake of completeness it is only the case to recall the definitions of the main mechanics, for the sake of completeness it is only the case to recall the definitions of the main quantities quantities addressed in this paper. With regard to volume-relationships, porosity, voids ratio, degree addressed in this paper. With regard to volume-relationships, porosity, voids ratio, degree of saturation, of saturation, and volumetric water content, they are defined as follows. By considering a soil and volumetric water content, they are defined as follows. By considering a soil element, porosity (n) is element, porosity (n) is the ratio of voids (pores) volume to total volume, while the voids ratio (e) is the ratio of voids (pores) volume to total volume, while the voids ratio (e) is the ratio of voids volume the ratio of voids volume to solid volume. The degree of saturation (Sr) is defined as the percentage to solid volume. The degree of saturation (Sr ) is defined as the percentage of the voids volume filled of the voids volume filled with water (Sr = 0 for a dry soil; Sr = 1 for a saturated soil; Sr < 1 for a partially with water (Sr = 0 for a dry soil; Sr = 1 for a saturated soil; Sr < 1 for a partially saturated soil). On the saturated soil). On the other hand, the most useful relationship between phases in terms of weights other hand, the most useful relationship between phases in terms of weights is the gravimetric water is the gravimetric water content of a soil element: in geotechnics and soil science it is defined as the content of a soil element: in geotechnics and soil science it is defined as the weight of water divided by weight of water divided by the weight of solid. the weight of solid. volumes weights gas (air) ≅0 liquid (water) soil Figure 1. Soil-phase relationships. Figure 1. Soil-phase relationships. Based on weight measures, the gravimetric water content is readily obtained in Based on a laboratory weight measures, the gravimetric water content is readily obtained in a laboratory environment: environment: Ww W − Ws w= = , (1) Ws W = = s , (1) This is accomplished by weighing the natural soil (w), drying it in an oven, then weighing the Thismeasuring dry soil, is accomplished by weighing the weight the natural ws , and computing thesoil ( ),content water drying according it in an oven, then weighing to Equation (1). Let the us define γdrymeasuring dry soil, as the drythe weight and unitweight γw computing , and the water as the unit weight content of water kN/m3 ):to Equation (1). Let according (10 us define as the dry unit weight and as the unit weight of water (≅10 kN/m3): Ws Ww γdry = =V γw = V=w , , (2) (2) Finally,the Finally, thevolumetric volumetricwater content(θ(θww))isisthe watercontent theratio ratioof ofthe thewater watervolume volumeto tothe thetotal totalvolume: volume: =Vw = ∙γdry (3) θw = = w · (3) V γw We underline in Equation (3) the gas volume of the soil is included in the dry unit weight . We underline in Equation (3) the gas volume of the soil is included in the dry unit weight γdry .
real part of permittivity (ε ) quantifies the amount of energy from an external electric field being stored in a material. The imaginary part of permittivity (ε ), also dubbed the “loss factor”, measures how dissipative or lossy a material is to an external electric field: ε > 0. Losses are associated with two main processes: molecular relaxation and electrical conductivity. Permittivity is dependent on (i) frequency, Sensors (ii)3585 2020, 20, moisture, and the (iii) salinity and ionic content of the soil. 4 of 14 A number of lossy dielectric mechanisms contribute to the global permittivity, being that the ionic and the rotational dipolar effects the most significant ones (see Figure 2). For increasing 3. Capacitive frequency only,Moisture the fastSensor mechanisms survive. Every cutoff frequency is characterized by a sudden decrease of and a peak In this paper, a capacitance of .probe has been used for sensing moisture. It is well known [35] that The electrical the output equivalent of a capacitive moisturecircuit of adepends sensors capacitive on the sensor always complex includes relative an element permittivity whose ε∗r of the soil capacitance can (dielectric medium): be written as: σdc ! 0 ∗ 00 ε∗r = ε0r − jεr = ε= 00 r − j ε + , (5) (4) relax 2π f ε0 where is a geometric 00 factor and is the permittivity in a vacuum. where ε0r and εr are the real and the imaginary part of the permittivity, respectively (Figure 2), σdc is The dielectric is able to store 00 energy when an external electric field is applied. If an AC sinusoidal the electrical conductivity, εrelax , the molecular relaxation contribution (dipolar rotational, atomic voltage source of frequency f is placed across the capacitor (no relaxation √ medium) a charging vibrational, current ic and and electronic a loss current energy il that arestates), relatedj istothe theimaginary numberwill−1, dielectric constant beand madef is up.the frequency. The real part of permittivity (ε0r ) quantifies the amount of energy from an external electric field being stored in a material. The imaginary part = = +(ε of permittivity 00 (6) r ), also dubbed the “loss factor”, measures 00 how where dissipative or lossyvelocity ω is the angular a material ω = is2πftoand an external electric v and i are phasors.field: εr > 0. Losses are associated with two main processes: molecular relaxation and electrical conductivity. Permittivity is dependent on (i) frequency, (ii) moisture, and the (iii) salinity and ionic content of the soil. Figure 2. Figure 2. Dielectric Dielectric mechanisms mechanisms contributing contributing toto dielectric dielectric behavior behavior atat the the microscopic microscopic level. level. Molecular Molecular relaxation (dipolar rotational, atomic vibrational and electronic energy states) have been highlighted. relaxation (dipolar rotational, atomic vibrational and electronic energy states) have been highlighted. With respect With respecttotoa similar a similar picture picture in [36], in [36], different different branches branches of drawn, of εr are 00 are corresponding drawn, corresponding to to different values of soil electrical conductivity σdc . Electromagnetic wave ranges have also been emphasized. A number of lossy dielectric mechanisms contribute to the global permittivity, being that the ionic and the rotational dipolar effects the most significant ones (see Figure 2). For increasing frequency only, the fast mechanisms survive. Every cutoff frequency is characterized by a sudden decrease of ε0r 00 and a peak of εr . The electrical equivalent circuit of a capacitive sensor always includes an element whose capacitance can be written as: C = ε∗r ε0 G0 (5) where G0 is a geometric factor and ε0 is the permittivity in a vacuum. The dielectric is able to store energy when an external electric field is applied. If an AC sinusoidal voltage source v of frequency f is placed across the capacitor (no relaxation medium) a charging current ic and a loss current il that are related to the dielectric constant will be made up. i = jωCv = ( jωε0r + ωεr )ε0 G0 v 00 (6)
Sensors 2020, 20, x FOR PEER REVIEW 5 of 14 Sensors 2020, 20, x FOR PEER REVIEW 5 of 14 different values of soil electrical conductivity . Electromagnetic wave ranges have also been Sensorsdifferent values of soil electrical conductivity 2020, 20, 3585 . Electromagnetic wave ranges have also been5 of 14 emphasized. emphasized. In Figure 3 the used commercial, blade-shaped, “Capacitive Soil Moisture Sensor v1.2” is whereIn is the angular ωFigure velocity 3 the used ω = 2πf and commercial, v and i are phasors. blade-shaped, “Capacitive Soil Moisture Sensor v1.2” is portrayed. In Figure portrayed. 3 the used commercial, blade-shaped, “Capacitive Soil Moisture Sensor v1.2” is portrayed. Figure 3. A “capacitive” soil moisture sensor. The grazing light image shows the coplanar concentric Figure Figure 3. A 3. capacitor Aof“capacitive” “capacitive” soil moisture the sensor. soil moisture sensor. sensor. The The grazing grazing light light image image shows shows the the coplanar coplanar concentric concentric capacitor of capacitor of the the sensor. sensor. To the best of our knowledge, a data sheet for this type of sensors is only available for version To the To 1.0 [37],the best of best of our manufacturedour knowledge, knowledge, by DFROBOT aa data dataand sheet sheet for this for this type advertised type with of the of sensors sensors name is only is only available for available SKU:SEN0193. for version version The only 1.0 1.0 [37],manufactured [37], significantmanufactured specifications by DFROBOT by DFROBOT given and in the and advertised advertised datasheet arewith the a powerwith name the supply name SKU:SEN0193. between SKU:SEN0193. 3.3The andonly The only 5.5 significant V, output significant specifications specifications given in the given in datasheet the are datasheet a power are supply a power between supply 3.3 between and 5.5 V, 3.3 output voltage between 0 and 3 V, and the recommended depth in soil. A detailed analysis of the electrical and 5.5 voltage V, output between 0voltage and 3 V, circuits between ofand the the 0recommended sensorand 3 V, was and depth initiallythe recommended in soil. A in accomplished depth detailed order in getsoil. analysis to A the of detailed acquainted analysis electrical on how of the circuits electrical of the the sensor sensor operates circuits was of 4).theaccomplished initially (Figure sensor was initially in orderaccomplished in order to get acquainted on tohowgetthe acquainted on how (Figure sensor operates the sensor 4). operates (Figure 4). Figure4.4.Schematic Figure Schematic of of the thecapacitive capacitive sensors. sensors. Resistance values are directly taken from component component Figure labels. 4. Schematic Capacitance of the values capacitive have been sensors. measuredResistance with an values are directly impedentiometer attaken 50 from kHz component after labels. Capacitance values have been measured with an impedentiometer at 50 kHz after removal removal from labels. afrom Capacitance a particular particular values samplesample and are have and been measured are affected affected with by ordinary by ordinary an impedentiometer manufacturing manufacturing errors.errors.at 50 kHz after removal from a particular sample and are affected by ordinary manufacturing errors. AAlow lowdropout dropout3.3 3.3V V voltage voltage regulator regulator supplies supplies aa TL555I CMOS timer whose output signal feeds feeds aalow lowApasslowfilter pass dropout filter(10(10kΩ3.3 Vresistor voltage kΩresistor and regulator andthethe supplies moisture moisture sensinga TL555I sensingcoplanarCMOS coplanar timer whose capacitor). capacitor). TheThe output main main signal function functionoffeeds this of athis lowstage stage ispass filter to produce (10 a kΩ resistor stationary and the sawtooth moisture sensing double-exponential coplanar waveform is to produce a stationary sawtooth double-exponential waveform whose average value iscapacitor). whose The main average function value is of the this the stage same same average is tovalue average produce value of the aofstationary the TL555I TL555I sawtooth output. output. However,double-exponential However, the peak peakwaveform thetopeak to ofwhose peak voltage voltage ofaverage the waveform value is the waveform depends the on same depends the average effective value of on thedielectric effective the TL555I dielectric constant of the output. constant ofHowever, soil. Then, thea soil. the peak peakThen, voltage a peakto peak detectorvoltage voltage of the detector provides the waveform provides analog the output depends analogthat signal onwethe output effective signal acquire thatdielectric through wethe constant acquire ADC of theofmicrocontroller. through the thesoil. ADC Then, of thea peak During voltage detector microcontroller. electrical During provides characterization the electrical with characterization analog the sensor outputprobe with in airthe signal wesensor that probe we acquire noted in that when air an through weoscilloscope noted the ADC that when thean ofprobe isoscilloscope introducedprobe microcontroller. During between is introduced electrical the 10 kΩ between characterization resistor andthethe10 kΩ withresistor diode, the then and the the sensor diode, probe output in then air voltage wethe output isnoted heavily thatvoltage when is modified, anheavily modified, oscilloscope thus indicating probe thatthusis the indicating introduced 14–18 pF that10 between and theMΩ 14–18 theof10thepF and kΩprobe 10significantly resistor MΩand ofthe thediode, probe modify significantly then thecircuit the output modify theThis voltage behavior. iscircuit canbehavior. heavily be easilyThis modified, thuscanindicating understood be easily since understood that the the 14–18 sensor since pF and capacitance the10 sensor inMΩ capacitance of air at the MHz 1.5 inofair theatorder probeissignificantly 1.5 modify MHz of 6.5ispF of(athe the order circuit value of 6.5 behavior. to be pF This confirmed (acan value to be bybefurther easily confirmed by understood measurement further since and the measurement sensor electromagnetic capacitanceand electromagnetic in air which simulations, at 1.5 areMHzsimulations, outisofofthe which thescope order ofofare the out 6.5 pFof(athe present valuescope paper). to of be the present confirmed The CPROBE bypaper). further measurement component of Figureand 4electromagnetic corresponds tosimulations, the section of which are out the sensor of the scope immersed in theof The CPROBE the present soil. However, paper). it mustcomponent be underlinedof Figure that 4CPROBE corresponds couldtonotthebesection directly ofassociated the sensortoimmersed the capacitancein the soil. of However, The Equation CPROBE(6).itInmust bethe component fact, underlined of Figure solder that4dielectric resist CPROBE corresponds could of thetonot thebesection sensor directly ofassociated separates thethe soiltofrom sensor the capacitance immersed in the the copper of Equation soil. However, electrodes of (5). the In it must fact, sensingbethe solder resist underlined capacitor dielectric that CPROBE and takes ofinthe partcould the sensor not separates be directly equivalent the soil associated electric tofrom circuit the the the copper of capacitance sensor. of Equation Moreover, this(5). In fact, the equivalent solder circuit resist should dielectric also include of the sensor parasitic separatesasthe capacitances, soil in shown from the the firstcopper figure of [35].
constant frequency, the Vout voltage will for sure depend on water content, porosity, and salinity/ionic content of the soil. The chosen “absolute value” measuring method is not capable of discerning among these contributions. Please note that there is no DC path to ground in the peak detector. In fact, the lower node of R4 is connected to a printed circuit board capacitor with parasitic AC interconnects to both GND6 of Sensors 2020, 20, 3585 and 14 Vcc and acts as a voltage divider between Vcc and GND at 1.5 MHz, being the parasitic capacitor impedance of the order of 30 to 60 kΩ, i.e., much smaller than the 1.8 MΩ or 880 kΩ series resistance. This The could peak detector of as be interpreted Figure 4 calculates design fault of theansensor. analogTo absolute confirmvalue of the waveform this conclusion, a recentmeasured (May 2020) on CPROBE at a constant frequency of about 1.5 MHz. The phase shift caused by batch of v.1.2 sensors shows that R4 is connected to the ground. We removed the passivation of two the CPROBE equivalent circuit sensorsisbelonging actually lost in the to the twoVout voltage different of thisFigure batches. peak detector. Therefore, 5 clearly shows a real and the missing R4 imaginary ground path partin of CPROBE cannot be distinguished the older version of the sensor. using this measurement method. If we add the fact that the equivalent circuitsignal The output of CPROBE is presently of the TL555I unknown,waveform is a trapezoidal the only conclusion running at we could about draw a 1.5 MHzis (Figure that at constant 6a). Thisfrequency, trapezoidal theprofile Vout voltage and thewill for sure related depend on waterduty-cycle out-of-specification content, porosity, of about and salinity/ionic 33% (the duty- content cycle of a 555 should always be greater than 50%) are likely caused by the close vicinity among of the soil. The chosen “absolute value” measuring method is not capable of discerning of the these contributions. operating frequency to the physical frequency limit for the TL555I device. On the other hand, it is well Please knownnote thatthat there issoil capacitive no DC path tosensors moisture groundshould in the operate peak detector. at a highIn fact, the lower frequency; the node higherof the R4 is connected to a printed circuit board capacitor with parasitic AC interconnects operating frequency, the lower the effect of losses related to the imaginary part of the permittivity. to both GND and Vcc and actsthe Moreover, as aslew voltage ratedivider between restraint of theVcc and GND waveform at 1.5inMHz, helps being thethe minimizing parasitic capacitor electromagnetic impedance of the order of 30 to 60 kΩ, i.e., much smaller than the 1.8 MΩ interference (EMI), which is possibly generated by the sensor in a non-ISM band and would be or 880 kΩ series resistance. This couldin beneficial becase interpreted as design fault of EMI compliance test.of the sensor. To confirm this conclusion, a recent (May 2020) batchFigure of v.1.26bsensors shows shows that R4 the double is connected exponential to the ground. waveform We removed on the anode the passivation of the diode of Figure 4 of twoa with sensors belonging to the two different batches. Figure 5 clearly 10 MΩ, 14–18 pF probe connected to the node when the sensor is suspended in air. shows the missing R4 ground path in the older version of the sensor. (a) (b) Figure 5. (a) Older and (b) recent soil moisture sensor v.1.2. The thin metal path to grounded capacitor Figure 5. (a) Older and (b) recent soil moisture sensor v.1.2. The thin metal path to grounded capacitor plate is clearly visible only in (b). plate is clearly visible only in (b). The output signal of the TL555I is a trapezoidal waveform running at about a 1.5 MHz (Figure 6a). This trapezoidal profile and the related out-of-specification duty-cycle of about 33% (the duty-cycle of a 555 should always be greater than 50%) are likely caused by the close vicinity of the operating frequency to the physical frequency limit for the TL555I device. On the other hand, it is well known that capacitive soil moisture sensors should operate at a high frequency; the higher the operating frequency, the lower the effect of losses related to the imaginary part of the permittivity. Moreover, the slew rate restraint of the waveform helps in minimizing the electromagnetic interference (EMI), which is possibly generated by the sensor in a non-ISM band and would be beneficial in case of EMI compliance test.
Sensors 2020, 20, 3585 7 of 14 Sensors 2020, 20, x FOR PEER REVIEW 7 of 14 (a) (b) Figure 6. Figure 6. (a) (a)TL555I TL555Ioutput output waveform. waveform. The The peak peak voltage voltage exceeds exceeds theV 3.3 the 3.3 V supply supply voltagevoltage of the of the TL555I. TL555I. (b) Double exponential waveform on the anode of the diode of Figure 4 measured (b) Double exponential waveform on the anode of the diode of Figure 4 measured with a 10 MΩ, with a 10 MΩ, 14–18 pF probe connected to the node when the sensor is suspended 14–18 pF probe connected to the node when the sensor is suspended in air. in air. Duty cycle Figure and output 6b shows voltage the double of nine different exponential waveformsensors (S1,anode on the S2, S5,ofS6, theS7, S9, S10, diode S13 and of Figure S14) 4 with awere electrically 10 MΩ, 14–18 pF characterized as a function probe connected of frequency. to the node when the Other sensorsensors (S3, S4,in is suspended S8,air. S11 and S12) were modified Duty by removing cycle and output the voltage TL555I ofandnineother related different components sensors in S6, (S1, S2, S5, order to S10, S7, S9, driveS13them and with S14) laboratory were waveform electrically generators, characterized or microcontroller as a function of frequency.boards. The sensor Other sensors (S3, S4,output S8, S11voltage and S12)ofwere the unmodified modified sensors was by removing themeasured TL555I and in other three related different “standard”inconditions: components sensor order to drive them suspended in air, with laboratory sensor suspended waveform generators,in air orwithin a Delrin® cylinder microcontroller boards. The(with 1.5” inner sensor output diameter voltage and of the3”unmodified height), andsensors sensor suspended was measuredin the samedifferent in three Delrin cylinder ® “standard”filled with distilled conditions: sensorwater. Resultsinare suspended air,shown sensorinsuspended Figure 7. in
Sensors 2020, Sensors 2020, 20, 20, 3585 x FOR PEER REVIEW 88 of of 14 14 air within a Delrin® cylinder (with 1.5” inner diameter and 3” height), and sensor suspended in the Sensors 2020, 20,®x FOR PEER REVIEW 8 of 14 same Delrin cylinder filled with distilled water. Results are shown in Figure 7. (%)cycle (%) duty cycleduty (a) (b) Figure 7. (a) Output voltage and (b) duty cycle as a function of frequency. (a) (b) Only a single sensor (S1) featured an operating frequency and a duty cycle differed from the other sensors (equal to 1.22 Figure (a)MHz 7. (a) Outputand 37.1%,and voltage respectively). (b) duty cycleThe duty cycle as aaaverage functionoperating of frequency. frequency.frequency and duty Figure 7. Output voltage and (b) as function of cycle of the other sensors were 1.53 MHz and 34.48%, respectively, with sample standard deviations of 1% Only andaa single 2.2%, sensor (S1) featured aninspection operating frequency the S1 and a duty cycle differed from the other Only singlerespectively. Electric sensor (S1) featured an operating of frequency sensor and adid dutynotcycle showdiffered any remarkable from the sensors difference(equal to 1.22 in (equal R2 and MHz and R3 resistance37.1%, respectively). values,respectively).The compared with average operating frequency and duty thatcycle other sensors to 1.22 MHz and 37.1%, The the otheroperating average sensors, frequency indicatingand the duty of the other component sensors most were probably 1.53 MHz and responsible 34.48%, for the respectively, “freak” behaviorwith sample was the standard C3 capacitor deviations or the of 1% TL555I cycle of the other sensors were 1.53 MHz and 34.48%, respectively, with sample standard deviations and 2.2%, respectively. Electric inspection of the S1 sensor did not show any remarkable difference in itself. of 1% and 2.2%, respectively. Electric inspection of the S1 sensor did not show any remarkable R2 and R3 resistance Figure values, compared with(difference the other sensors, indicating that the component most difference in8 shows R2 andthe R3sensor output resistance range values, compared between with the output voltage other sensors, inindicating air and in that distilled the probably water) asresponsible a most function forduty of the “freak” cycle atbehavior 1.5for MHz wasonethe C3thecapacitor or the TL555I itself.by an external component probably responsible the for “freak”of modified behavior was the sensors, driven C3 capacitor or the TL555I Figure generator. waveform 8 shows theAsensorsquareoutput input range (difference waveform between was used in thisoutput case,voltage with ain3.3 airVandpeakin distilled to peak itself. water) voltage, as a function slightly lowerof duty cycle at 1.5 MHz for one of the modified sensors, driven by an external Figure 8 shows the than sensor theoutput peak voltage of Figure between range (difference 6. The peak of the output output voltage inrange air and ofinthe sensor distilled waveform lays around generator. duty A square cycle = 37%, input waveform which was was used slightly in this higher than case, the with a 3.3 duty average V peak to peak cycle of voltage, the non- water) as a function of duty cycle at 1.5 MHz for one of the modified sensors, driven by an external slightly lower modified sensors. than the peak voltage of Figure 6. The peak of the output range of the sensor lays around waveform generator. A square input waveform was used in this case, with a 3.3 V peak to peak duty cycle = 37%, which was slightly higher than the average duty cycle of the non-modified sensors. voltage, slightly lower than the peak voltage of Figure 6. The peak of the output range of the sensor lays around duty cycle = 37%, which was slightly higher than the average duty cycle of the non- modified sensors. Figure 8. Difference between output voltage in air and in distilled water as a function of duty cycle at Figure 8. Difference between output voltage in air and in distilled water as a function of duty cycle at 1.5 MHz for a modified sensor driven by laboratory instrumentation. 1.5 MHz for a modified sensor driven by laboratory instrumentation. For the characterizations of the rest of the paper, we used only two sensors featuring operating For the characterizations of the rest of the paper, we used only two sensors featuring operating frequency Figureand duty cycle 8. Difference shown between in Table output 1. in air and in distilled water as a function of duty cycle at voltage frequency and duty cycle shown in Table 1. 1.5 MHz for a modified sensor driven by laboratory instrumentation. For the characterizations of the rest of the paper, we used only two sensors featuring operating frequency and duty cycle shown in Table 1.
Sensors 2020, 20, x FOR PEER REVIEW 9 of 14 Table 1. Selected sensors characteristics. id. f (MHz) Duty Cycle Sensors 2020, 20, 3585 S2 1.53 35.6% 9 of 14 S10 1.51 35% 4. Experimental Characterization with Table Silica Sandy 1. Selected sensorsSoil characteristics. The experimental characterization id. was planned with f (MHz) theCycle Duty aim to understand how the chosen sensor works in a well-controlled S2 soil environment. 1.53 To this purpose, 35.6% we chose to operate with a clean silica sandy soil. In particular, theS10 mineralogical 1.51 constituents of 35% the soil were SiO2 96% mi., Fe2O3 1% max, Al2O3 0.5% max, CaO + MgO 1.5% max, and Na2O + K2O 1.0% max. Commercial distilled water was used for sample 4. Experimental preparation. with Silica Sandy Soil Characterization We chose to prepare our samples using the gravimetric water content (GWC) principle. For volumeThemeasurements, experimental characterization we used a 1000was mLplanned graduatedwith the aimwith cylinder to understand how the 20 mL grading chosen sensor divisions. works in a well-controlled soil environment. To this purpose, we chose to operate with a clean silica sandy soil. Calibration 4.1. Sensor In particular, the with mineralogical Constant GWC constituents of the soil were SiO2 96% mi., Fe2 O3 1% max, Al2 O3 0.5% max, CaO + MgO 1.5% max, and Na2 O + K2 O 1.0% max. Commercial distilled water was The first experimental characterization of our work regarded the study of the sensor response used for sample preparation. with constant GWC of 7.5% using the total sample volume as a parameter. Dry sand was prepared in We chose to prepare our samples using the gravimetric water content (GWC) principle. For volume an oven at 110 °C, then 950 g of material was poured into the graduated cylinder and a gravimetric measurements, we used a 1000 mL graduated cylinder with 20 mL grading divisions. 7.5% of water was added (Figure 9). The sample was mixed by hand. Then five different samples wereSensor 4.1. prepared in sequence, Calibration through with Constant GWC dynamic compaction in a graduated cylinder by means of a mortar. For each volume, two capacitive sensors were driven into the soil in two different positions The first experimental characterization of our work regarded the study of the sensor response and repeated measurements were acquired. with constant GWC of 7.5% using the total sample volume as a parameter. Dry sand was prepared in It should be underlined that the active volume of influence of the coplanar sensor capacitor was an oven at 110 ◦ C, then 950 g of material was poured into the graduated cylinder and a gravimetric much smaller than the total volume of the sample placed in the graduated cylinder. 7.5% of water was added (Figure 9). The sample was mixed by hand. Then five different samples were Sensors were kept sufficiently far apart in order to have no mutual influence between their prepared in sequence, through dynamic compaction in a graduated cylinder by means of a mortar. measurements. After insertion of one sensor in the soil, we determined the minimum distance such For each volume, two capacitive sensors were driven into the soil in two different positions and that the introduction of the second sensor did not change the reading of the first one. repeated measurements were acquired. (a) (b) (c) Figure 9. Figure 9. The Thegraduated graduatedcylinder: cylinder:(a) (a)nonocompaction; compaction;(b)(b) maximum maximum compaction, compaction, soilsoil volume volume is mL; is 620 620 mL; (c) (c)volume soil soil volume is 680ismL 680with mL with two sensors two sensors driven driven into. into. It should be underlined that the active volume of influence of the coplanar sensor capacitor was much smaller than the total volume of the sample placed in the graduated cylinder.
Sensors 2020, 20, 3585 10 of 14 SensorsSensors 2020, 20, xwere keptREVIEW FOR PEER sufficiently far apart in order to have no mutual influence between10their of 14 measurements. After insertion of one sensor in the soil, we determined the minimum distance such Figure that the 10 shows the introduction obtained of the secondmeasurement sensor did notresults. changeDifferent colors the reading of represented the first one.the two sensors. Repeated Figuremeasurements of a single 10 shows the obtained sensor appeared measurement results.toDifferent be verycolors precise, with verythe represented low twostandard sensors. deviation (always lower of Repeated measurements than 3.3 mV). a single sensorHowever, appearedmeasurements of the to be very precise, twovery with sensors differed low standard significantly, evenlower deviation (always more than than 3.3 5%.mV). ThisHowever, was mostmeasurements likely due to theof non-uniformity of the water the two sensors differed content significantly, within the soil even more thansample; 5%. Thispresumably, the two was most likely sensors due to thecaught soil regions non-uniformity of in thethe measurement water cylinder content within the whereas, due to a different distribution of the pores filled with water, the water content soil sample; presumably, the two sensors caught soil regions in the measurement cylinder whereas, differed too. due to a different distribution of the pores filled with water, the water content differed too. sensor output voltage (V) Figure 10. Sensor Sensor output output voltage voltage as as a function of soil volume at constant gravimetric water content (GWC) of 7.5%. The results resultsshown shownin in Figure 10 indicate Figure that sample 10 indicate preparation that sample strongly strongly preparation influencesinfluences the capacitive the sensor measurements. capacitive Different levels sensor measurements. of soil sample Different levels compaction induced of soil sample significant compaction relative significant induced differences of the sensor relative output voltage. differences For thisoutput of the sensor reason, voltage. we decided Fortothis continue thewe reason, experimental decided to characterization continue the using a constant experimental sample volume. characterization using a constant sample volume. 4.2. Sensor 4.2. Sensor Calibration Calibration at at Constant Constant Soil Soil Volume Volume When the When the capacitive capacitive sensor sensor volume volume of of influence influence diddid not not change change during during measurements, measurements, we we supposed we supposed we operated operated at at aa constant constant soilsoil volume. volume. ThisThis experimental experimental section section is is devoted devoted to to constant constant volume measurement with GWC volume measurement with GWC as a parameter. as a parameter. Similarly, to ◦ C, Similarly, to the the procedure proceduredescribed describedininSection Section4.1, dry 4.1., drysand sand was prepared was prepared in an oven in an at 110 oven at 110 thenthen °C, 950 950 g of gsilica sandsand of silica was poured into the was poured graduated into cylinder, the graduated and eight cylinder, anddifferent weights weights eight different of distilled of water were added, spaced of 2.5%, starting from 2.5% up to 20.0%. The distilled water were added, spaced of 2.5%, starting from 2.5% up to 20.0%. The soil-water mixture soil-water mixture were obtained were by hand. obtained Then,Then, by hand. each sample each sampletwo capacitive sensors two capacitive were driven sensors into the were driven soilthe into sample in two soil sample different positions and repeated measurements were acquired. in two different positions and repeated measurements were acquired. Figure 11 shows the Figure 11 shows the experimental results. The correlation experimental results. The coefficient correlation of the data wasof−0.945, coefficient the datarelatively far from was −0.945, −1, suggesting relatively far from a low −1, probability afor suggesting low a linear relationship probability between for a linear the output relationship voltage between and the the GWC. output voltageError andbars represented the GWC. Error threerepresented bars times the sample standard three times deviation, the sample i.e., 0.124 standard V, calculated deviation, i.e., 0.124with respect towith V, calculated a second respectorder to a fitting polynomial V = A · GWC 2 + B · GWC + C. An additional three-parameter exponential fitting second order fittingoutpolynomial = ∙ + ∙ + . An additional three-parameter Vout = A · eGWC/B exponential fitting+ C was = also / ∙ attempted + and waswe obtained also a veryand attempted similar we threefold obtained sample a verystandard similar deviation of 0.122. threefold sample standard deviation of 0.122.
Sensors 2020, 20, 3585 11 of 14 Sensors 2020, 20, x FOR PEER REVIEW 11 of 14 Figure 11. Figure 11. Sensor output voltage as a function of of GWC GWC at at constant constant soil soil volume. volume. 5. 5. Discussion Discussion The The experimental experimental results results shown shown in in Figure Figure 10 10 clearly clearly show show that that that that porosity porosity severely severely affects affects capacitive capacitive soil moisture measurements. If not properly taken into account, this undoubtedly soil moisture measurements. If not properly taken into account, this effect could effect could invalidate undoubtedly theinvalidate results of this type ofof the results measurements. Referring to the this type of measurements. experimental Referring data of Figuredata to the experimental 11, even if the error bars are relatively wide, the constant volume concept helped us obtain of Figure 11, even if the error bars are relatively wide, the constant volume concept helped us obtain a well-defined trend of the output a well-defined trendvoltage as a function of the output voltage of as GWC, withofa GWC, a function positive influence with on influence a positive the accuracy of on the the measurement. accuracy of the measurement. We Wepresently presentlydodo notnot know precisely know the active precisely volume the active of influence volume of the coplanar of influence sensor capacitor. of the coplanar sensor Measurements and electromagnetic capacitor. Measurements simulationssimulations and electromagnetic are in progress. However, are in progress.we can assume However, we this can volume assume as a constant this volume as even in situ applications, a constant even in situ at least for theatnecessary applications, least for period of measurement, the necessary in the absence period of measurement, of soil volume changes due to any applied actions, including environmental in the absence of soil volume changes due to any applied actions, including environmental loads. loads. Results Results shown shown in in Section Section 4.2 4.2.demonstrate demonstratethat, that,at atleast leastfor foraa well-defined well-defined typetype of of soil, soil, silica silica sand sand in case, for a constant γ in this case, for a constant dry (see again Equation (2)), coplanar capacitive sensors yielded aa reliable this (see again Equation (2)), coplanar capacitive sensors yielded reliable relationship relationship between between output output voltage voltage and and gravimetric gravimetric water water content. In addition, content. In addition, for for aa given given soil, soil, viz. viz. for a given value for a given value of of γdry again, due to Equation (3) our measurements could bring to again, due to Equation (3) our measurements could bring to a correspondinga corresponding estimate estimate ofof the the volumetric volumetric water water content. content. Of course, for Of course, for different different types types of of soil soil and and for different values for different values of the dry unit weight, a calibration is required. of the dry unit weight, a calibration is required. 6. Conclusions 6. Conclusions The experimental and accurate determination of soil moisture or soil water content is a matter of The experimental and accurate determination of soil moisture or soil water content is a matter great importance in different scientific fields. In this paper, a commercial “capacitive” soil moisture of great importance in different scientific fields. In this paper, a commercial “capacitive” soil moisture sensor typically housed in low-cost distributed nodes for IoT applications was experimentally sensor typically housed in low-cost distributed nodes for IoT applications was experimentally characterized in order to get acquainted on how the sensor operates. A detailed analysis on the sensor’s characterized in order to get acquainted on how the sensor operates. A detailed analysis on the electrical circuit was initially carried out. The sensor response with constant GWC using a varying sensor’s electrical circuit was initially carried out. The sensor response with constant GWC using a sample volume was investigated. The obtained results indicated that sample preparation strongly varying sample volume was investigated. The obtained results indicated that sample preparation influenced the capacitive sensor measurements; different levels of soil sample compaction induced strongly influenced the capacitive sensor measurements; different levels of soil sample compaction significant relative differences of the sensor output voltage. For this reason, constant sample volume induced significant relative differences of the sensor output voltage. For this reason, constant sample characterizations were carried out and a well-defined trend of the output voltage as a function of volume characterizations were carried out and a well-defined trend of the output voltage as a GWC was found, as shown in Equation (2). Even if the error bars are relatively wide, the constant function of GWC was found, as shown in Equation (2). Even if the error bars are relatively wide, the volume concept helped us obtain reproducible results, with a positive influence on the accuracy of constant volume concept helped us obtain reproducible results, with a positive influence on the the measurement. Therefore, at least for a well-defined type of soil at constant volume, the coplanar accuracy of the measurement. Therefore, at least for a well-defined type of soil at constant volume, capacitive sensors yielded a reliable relationship between output voltage and GWC. Although the the coplanar capacitive sensors yielded a reliable relationship between output voltage and GWC. experimental investigation is still in progress, the results obtained from this study appear to be Although the experimental investigation is still in progress, the results obtained from this study appear to be promising. A possible use of such capacitive sensors for water content measurements in the field will be the object of further research.
Sensors 2020, 20, 3585 12 of 14 promising. A possible use of such capacitive sensors for water content measurements in the field will be the object of further research. Author Contributions: Conceptualization, P.P., M.C. and A.S.; Methodology, P.P., A.G., M.C. and A.S.; Software, A.G. and A.S.; Validation, P.P., L.G., A.G., M.C. and A.S.; Formal analysis, P.P., L.G. and A.S.; Investigation, P.P., L.G., A.G., M.C. and A.S.; Data curation, P.P., L.G., A.G. and A.S.; Writing—original draft, P.P., M.C. and A.S.; Writing—review & editing, P.P., M.C. and A.S.; Supervision, P.P.; Project administration, P.P.; Resources, P.P.; Funding acquisition, P.P., M.C. and A.S. All authors have read and agreed to the published version of the manuscript. Funding: This research was partly funded by the Department Of Engineering of the University of Perugia, Italy, grants “Ricerca di base 2017”, “Ricerca di base 2018” and “Ricerca di base 2019”. Acknowledgments: Thanks to D. Grohmann and G. Marconi for helpful discussions and technical support. The technical assistance of Alessandro De Luca, Carmine Villani delle Vergini, Diego Fortunati, Nicola Papini, Francesco Gobbi and Elena Burani is also gratefully acknowledged. Conflicts of Interest: The authors declare no conflict of interest. References 1. Perera, C.; Zaslavsky, A.; Christen, P.; Georgakopoulos, D. Sensing as a service model for smart cities supported by Internet of Things. Trans. Emerg. Tel. Tech. 2014, 25, 81–93. [CrossRef] 2. Rajab, H.; Cinkelr, T. Internet of Things for Smart Cities. In Proceedings of the 2018 International Symposium on Networks, Computers and Communications (ISNCC), Rome, Italy, 19–21 June 2018. 3. Zgank, A. Bee Swarm Activity Acoustic Classification for an IoT-Based Farm Service. Sensors 2019, 20, 21. [CrossRef] [PubMed] 4. Saab, M.T.A.; Jomaa, I.; Skaf, S.; Fahed, S.; Todorović, M. Assessment of a Smartphone Application for Real-Time Irrigation Scheduling in Mediterranean Environments. Water 2019, 11, 252. [CrossRef] 5. Ruiz-Garcia, L.; Lunadei, L.; Barreiro, P.; Robla, I. A Review of Wireless Sensor Technologies and Applications in Agriculture and Food Industry: State of the Art and Current Trends. Sensors 2009, 9, 4728–4750. [CrossRef] [PubMed] 6. Magalotti, D.; Placidi, P.; Dionigi, M.; Scorzoni, A.; Servoli, L. Experimental Characterization of a Personal Wireless Sensor Network for the Medical X-Ray Dosimetry. IEEE Trans. Instrum. Meas. 2016, 65, 2002–2011. [CrossRef] 7. Khan, A.; Aziz, S.; Bashir, M.; Khan, M.U. IoT and Wireless Sensor Network based Autonomous Farming Robot. In Proceedings of the 2 2020 International Conference on Emerging Trends in Smart Technologies (ICETST), Karachi, Pakistan, 26–27 March 2020. 8. Wang, N.; Zhang, N.; Wang, M. Wireless sensors in agriculture and food industry—Recent development and future perspective. Comput. Electron. Agric. 2006, 50, 1–14. [CrossRef] 9. Surendran, D.; Shilpa, A.; Sherin, J. Modern Agriculture Using Wireless Sensor Network (WSN). In Proceedings of the 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), Coimbatore, India, 15–16 March 2019. 10. González-Teruel, J.D.; Sánchez, R.T.; Blaya-Ros, J.; Toledo-Moreo, A.B.; Jiménez-Buendía, M. Design and Calibration of a Low-Cost SDI-12 Soil Moisture Sensor. Sensors 2019, 19, 491. [CrossRef] 11. BeechamRes. Towards Smart Farming Agriculture Embracing the IoT Vision. Available online: https: //www.beechamresearch.com/files/BRL%20Smart%20Farming%20Executive%20Summary.pdf (accessed on 24 June 2020). 12. Yelamarthi, K.; Aman, M.S.; Abdelgawad, A. An Application-Driven Modular IoT Architecture. Wirel. Commun. Mob. Comput. 2017, 1–16. [CrossRef] 13. Zyrianoff, I.D.; Heideker, A.; Silva, D.O.; Kleinschmidt, J.H.; Soininen, J.P.; Cinotti, T.S.; Kamienski, C. Architecting and Deploying IoT Smart Applications: A Performance-Oriented Approach. Sensors 2020, 20, 84. [CrossRef] 14. Chartzoulakisa, K.; Bertaki, M. Sustainable Water Management in Agriculture under Climate Change. Agric. Agric. Sci. Procedia 2015, 4, 88–98. [CrossRef]
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