Implementation a of 3-axis inclinometer for RTK-GPS antenna correction of EMI sensor platform A. Jardúo1), M. Perez-Ruiz1), K. Vanderlinden2), J. Agüera3) 1)
Universidad de Sevilla. Área de Ingeniería Agroforestal. Dpto. de Ingeniería Aeroespacial y Mecánica de Fluidos. Ctra. Sevilla-Utrera, km 1, 41013 Sevilla. E-Mail:
[email protected] 2) IFAPA, Centro Las Torres-Tomejil, Ctra. Sevilla-Cazalla, km 12.2, 41200 Alcalá del Río (Sevilla). 3) Dpto. Ingeniería Rural, ETSIAM. Universidad de Córdoba, Campus Universitario de Rabanales, Edificio Leonardo da Vinci, Carretera de Madrid NIV, Km.396, 14014-Córdoba.
Abstract Geophysical sensor measurements are often made by towing a mobile platform over the soil surface. Surface irregularity and inadequate travel speed can significantly reduce the stability of the platform and negatively affect the quality of the measurements. The objective of this study was implemented and developed a methodology to correct the RTK-GPS location of a mobile sensor platform for continuous ECa measurements with associated GPS locations at discrete time intervals. A 3-axis inclinometer was mounted directly above the EMI sensor and below the GPS antenna to monitor platform pitch, roll and yaw. Northing and Easting showed similar percentages of correction 30%. The results indicate that elevation measurements for generating DEMs, and geo-referencing geophysical measurements require additional correction for antenna deviations. Key words: Geophysical sensor, soil apparent electrical conductivity (ECa), inclinometer, precision agriculture
1. Introduction One objective of precision agriculture is to improve the accurate information about soil and crop properties to optimize management of agricultural inputs according to economically justified local needs. Soil parameters are neither static nor homogenous in space and time, however analytical costs are often a limiting factor when attempting to address spatial soil variability especially in large-scale applications (Viscarra-Rossel and McBratney 1998). The traditional way to explore in field soil variation is through grid-sampling, which is time consuming, labour-intensive and lacks spatial exhaustiveness. An ideal way to obtain soil data at a higher spatial resolution would be to use on-the-go proximal soil sensors (Adamchuk et al., 2004). The apparent soil electrical conductivity (ECa) map of a farm or field, obtained using geophysical methods, is often signicantly correlated with the crop yield map for the same field (Barry et al., 2008) and is the most used proximal soil sensor in agriculture (Sudduth et al., 2005; Lesch et al., 2005; Martínez et al., 2009). Overall, the adoption of geophysical measurements in agriculture, particularly electromagnetic induction methods (EMI), has been motivated by the need for non-destructive, fast, real-time and inexpensive measurements using mobile platforms, as compared to drilling or excavation, for topsoil profile characterization (≈ 2 m). Many studies found ECa measurements, provided by EMI sensors to be useful for estimating the spatial variability of soil properties such as soil water content, clay content or organic matter content, (Corwin and Lesch, 2005; Martinez et al., 2009;
2010). The EMI measurements are made with the device at or above the soil surface and its basic mechanism is explained by Fraday´s Law of electromagnetic induction. A transmitter electromagnetic coil located at one end of the instrument induces circular eddy-current loops in the soil. The magnitude of these loops is directly proportional to the ECa of the soil in the vicinity of that loop. Each current loop generates a secondary electromagnetic field that is proportional to the current flowing within the loop. The receiver coil intercepts a portion of the secondaryinduced electromagnetic field from each loop, and the sum of these signals is related to a depthweighted ECa (Corwing and Lesch, 2005). ECa represents the ability of soil media to conduct an electrical charge and characterizes soil profiles to a depth defined by the geometry of the measuring instrument. EMI-based ECa measurements with the EM-38 sensor (Geonics Limited, Mississauga, Ontario, Canada) have been used by numerous researchers to characterize soils of an agricultural field with respect to different soil factors, including soil texture and salinity (Dabas and Tabbagh, 2003; Freeland et al. 2002). Kitchen et al. (2005) investigated the effectiveness of using EMI mapping for delineating productivity zones for agricultural management in claypan soils of Missouri. The work showed that the productivity zones delineated using ECa and elevation data agreed up to 70% with those delineated from 10 years of combine monitored yield maps. In the last decade, the integration of Global Navigation Satellite Systems (GNSS) with sensors for off-road vehicle systems and others platforms provide real-time sub-meter or even centimeter-level accuracy, and revolutionized precision agriculture (Perez-Ruiz et al., 2011). The GNSS receivers are a key part of the precision agriculture technologies as position information is a prerequisite for site-specific crop management. However, so far the researchers held that not all of tasks that are or can be performed in precision agriculture needed the same level of GNSS accuracies (Wilson 2000; Perez-Ruiz et al., 2011). Some precision agriculture applications such as yield monitoring, soil samples or variable rate applications, are property performed with carrier-phase differential correction and errors below 1 m. Currently, Real-time Kinematic-Global Positioning System (RTK-GPS) technology offers the possibility of transitioning site-specific techniques from sub-meter level precision to centimetre-level precision. Although differential correction signals (DGPS) have been used satistactorily to geo-position electromagnetic induction (EMI), elevation, or gamma ray spectrometry (GRS) measurements, accuracies of ±10 might be insufficient. Accurate measurements (±2 cm) of terrain elevation for DEM construction and geo-referencing geophysical measurements make it possible to correct for sensor errors. Theoretically, knowing the topography and the sensor travel direction the effect of terrain irregularities and inclination on the sensor measurements could be corrected. As the resolution at which accurate the geo-positioning can be achieved improves, the possibility of using successfully EMI sensor in agriculture increases. Kravchenko (2002) studied the possibility of using soil geo-referenced ECa data and topographical information for predicting soil drainage classes. In this work an elevation map was obtained using RTK-GPS, and terrain slope, ECa, and distance to drainage were identified as the most useful secondary variables for predicting soil drainage classes. The purpose of this work is to implement and develop a methodology to correct the RTK-GPS location of a mobile sensor platform for continuous ECa measurements with associated GPS locations at discrete time intervals (typically 1 per second).
2. Material and Methods 2.1 Global positioning system RTK systems are the most accurate solution for GNSS (Global Navigation Satellite System) applications (2 cm accuracy). An RTK system requires two receivers, a radio link, and embedded navigation controller that integrates rover sensors and GPS data to compute the final position of the rover receiver (Misra & Enge, 2006). In this study, and rover RTK-GPS receiver (AgGPS 432, Trimble Navigation Ltd., Sunnyvale, CA, USA) was used to georreference measurements of a commercial electromagnetic induction sensor (EM38-DD, Geonics, Mississauga, ON, Canada). The GPS antenna of this receiver was mounted on top of the nonmetal platform (about 1 m above the soil surface). The system utilized an RTK-GPS correction signal from a local (located about 300 m from the test site) GPS base station (Trimble Ag RTK Base 430) to obtain RTK Fixed quality accuracy. A PPS (pulse per second) signal, was produced by the GPS receiver to synchronize the geoposition data with external events. The RTK-GPS receiver was set to output the “NMEA0183 GPGGA” string containing the geographic coordinates (Latitude and Longitude) at 1 Hz rate via an RS-232 serial connection. 2.2 Device design A non-metal platform was used to tow the soil conductivity meter as described by Martínez et al. (2009). A 3-axis inclinometer (model VN-100, VectorNav Technologies, LLC. Richardson, TX, USA), was retrofitted directly above the EMI sensor and below the GPS antenna to monitor platform pitch, roll and yaw. The sensor was capable of measuring pitch, roll and yaw relative to level with a resolution of 0.00017 radians (corresponding to a 0.5 mm resolution parallel and perpendicular to the direction of travel at the ground level) and was used to provide ground level offset correction of location due to platform tilt. The sensor outputs were automatically recorded by a handheld PC (model TK 6000, Titan Elite, Inc., New York, USA) at each sampling point. Through the RS-232 serial ports (DB9) COM1 and COM2 communication with the EMI sensor and GPS receiver was established. Figure 1 shows the system in operation. RTK-GPS antenna
3-axis inclinometer
Plastic (PVC) Platform EM38 Inside
Figure 1. Non-metal platform used for ECa
2.3 Lab and field experiments In a first in-door test, the 3-axis inclinometer sensor was calibrated by dropping a plumb bob from the location of the inclinometer sensor and finding the relationship between the angle value and the deviation of the plumb bob (Fig. 2). A line gauge was used to know the deviation in millimeters. This relationship was then used to determine the pitch and roll offset in both the Northing and Easting directions. Field tests were conducted on flat and bare soil. The field site was located at the IFAPA Las Torres-Tomejil Center in southwest Spain (Latitude: 237921,13 N, Longitude: 4155955,93 W). Theses test were carried out during three days in the spring of 2012. The mobile platform was placed in 51 random locations on the ground, completely vertical, as indicated by a bubble level located on the system. Measurements of coordinates and angles have been performed in this situation. We applied a random inclination to the GPS antenna pole and maintained this inclination as measurements were taken. This process was repeated randomly across the field at 51 locations. 3. Results Once the GPS antenna, inclinometer and EM38 sensor were assembled on the static platform, used for the inclinometer calibration, the system was tested in the lab by simulating situations that might occur in the field (Fig. 2). All the mechanical and electrical system components including cables, voltage levels, power, communication equipment were in place during the experiments. The calibration tests demonstrated good performance based on the coefficient of determination (R2) for pitch (cm) and roll (cm) measurements. In all trials, a value close to 1 was obtained (R2 = 0.996 for pitch; R2 = 0.998 for roll).
(t1) 3-axis Inclinometer
RTK-GPS Antenna
(t2)
Line gauge (mm) EM 38 Sensor Figure 2. Schematic representation of the experiments. The 3-axis inclinometer was calibrated relating the deviation from center of the plumbob to the angle output of the inclinometer sensor.
In the field test, 51 locations point were logged in different rows in the study. The mounting location of the GPS antenna allowed good satellite geometry to be obtained during the experiment and RTK GPS fixed quality was obtained for all antenna positions recorded during surveying. The data in Table 1 shows the mean and percentage of displacement corrected values for Northing and Easting between actual GPS antenna positions and GPS antenna position estimates generated using the platform inclination. Comparison of these results show that, as expected for a 1.5 m GPS antenna height, a significant increase in performance was obtained when GPS measurements were corrected for platform inclination (greater than 30% error reduction). The results show that correction for inclination is very important when attempting to develop accurate maps, in agreement with the observations of Norremark et al. (2003). The use of an inclination sensor allowed the GPS antenna to be mounted at a height selected to maximize the potential for access to high quality satellite geometries and to minimize the potential for GPS multipath error due to the tractor. Tabla 1. Displacement of GPS-RTK antenna without and with pitch and roll correction Northing
Easting
Days
Locations
Mean Displacement (cm)
1
20
0.332
Mean Displacement with tilt correction (cm) 0.217
2
11
0.390
0.261
33.08
0.392
0.289
26.27
3
20
0.258
0.180
30.00
0.379
0.278
28.01
Total
51
0.326
0.219
32.57
0.431
0.308
28.66
% Corrected
Mean Displacement (cm)
34.63
0.523
Mean Displacement with tilt correction (cm) 0.357
% Corrected
For example, the mean displacement an average of 0.32 in Northing dropped from to 0.22 cm when the GPS antenna locations were corrected for platform inclination. The mean displacement in Easting improved from an average of 0.43 cm to 0.31 cm. Both, Northing and Easting shows similar percentages of correction 30%. This similarity is not unexpected due to the fact that the travel direction was zero. These levels of performance are similar to those obtained by Ehsani et al. (2004), and Sun et al. (2010) in their work on automatic RTK GPS mapping of tomato during planting. 4. Conclusions Preliminary results are present for real-time correction of the GPS antenna deviation due to uneven terrain. The following conclusions were drawn based upon the results of this research: - This valuable correction of GPS-RTK antenna deviation will be used to provide applications for the use of this equipment in different environments (slope, soil type, etc.). Although literature generally concludes that differential GPS technology is sufficient for this kind of application, our results indicate that elevation measurements for generating DEMs, and geo-referencing geophysical measurements require additional correction for antenna deviations.
31.72
- Work with GPS-RTK dedicated base station removes many problems associated with the loss of the signal from the differential GPS systems (e.g. tree obstacles, etc.). - The adoption of new procedures and sensor technologies that optimize farm operations will assist the agricultural sector to remain competitive in a global economy. Improving our understanding the field-behavior of these sensors will expand our possibilities to measure accurately in rough terrain. 5. Acknowledgements This research was made possible thanks to funding provided by the University of Seville, the Spanish Ministry of Science and Innovation and FEDER through grant AGL2009-12936-C03-03 and by the Junta de Andalucía though grant AGR-4782. Also mobility grant PR2010-0191 of the Spanish Ministry of Education is acknowledged. 6. References Adamchuk, V.I., Hummel, J.W., Morgan, M.T. & Upadhyaya, S.K. (2004). On-the-go soil sensor for precision agriculture. Computers and Electronics in Agriculture, 44:71-91. Barry, J. A., Douglas Groom, M., Reza, E., & Jeffrey, J. D. (2008). Resistivity methods. In J. A. Barry, J. D. Jeffrey, & M. R. Ehsani (Eds.), Handbook of agricultural geophysics (pp. 86–91). Boca Raton, FL: CRC Press. Corwin, D.L., Lesch, S.M. 2005. Apparent soil electrical conductivity measurements in agriculture. Computers and Electronics in Agriculture, 46:11-43. Dabas, M. & Tabbagh, A., 2003. A comparison of EMI and DC methods used in soil mapping theoretical considerations for precision agriculture. In: Stafford, J., Werner, A. (Eds.), Precision Agriculture. Wageningen Academic Publishers, Wageningen, The Netherlands, pp. 121–127. Ehsani, M. R., Upadhyaya, S. K. & Mattson, M. L. (2004). Seed location mapping using RTK GPS. Trans. ASAE 47(3) 909–914. Freeland, R.S., Yoder, R.E., Ammons, J.T. & Leonard, L.L., (2002). Mobilized surveying of soil conductivity using electromagnetic induction. Appl. Eng. Agric. 18 (1), 121–126. Kitchen, N.R., Sudduth, K.A., Myers, D.B., Drummond, S.T. & Hong, S.Y. (2005). Delineating productivity zones on claypan soil fields using apparent soil electrical conductivity. Comp. Electron. Agric. 46, 285–308. Kravchenko, A.N., Bollero, G.A., Omonode, R.A. & Bullock, D.G. (2002). Quantitative mapping of soil drainage classes using topographical data and soil electrical conductivity. Soil. Sci. Soc. Am. J. 66: 235-243. Lesch, S.M., (2005). Sensor-directed response surface sampling designs for characterizing spatial variation in soil properties. Comp. Electron. Agric. 46, 153–179. Martínez, G., Vanderlinden, K., Espejo, A., Giráldez,J.V., Muriel, J.L. (2010). Field-scale soil moisture pattern mapping using electromagnetic induction. Vadose Zone Journal, 9: 871-881. Martinez, G., Vanderlinden, K., Ordóñez, R., Muriel, J.L. 2009. Can apparent electrical conductivity improve the spatial characterization of soil organic carbon? Vadose Zone Journal, 8:586–593. Misra, P. & Enge, P. (2006). Global position systems: Signals, measurements and performance (2nd ed.). Lincoln, MA, USA: Ganga-Jamuna Press. Nørremark, M., Griepentrog, H. W., Nielsen, H., & Blackmore, B. S. (2003). A method for high accuracy geo-referencing of data from field operations. In J. V. Stafford, & A. Werner (Eds.), Proceedings of the 4th European Conference on Precision Agriculture. Berlin, Germany. Perez-Ruiz, M., Carballido, J., Agüera, J. and Gil, J. A. 2011. Assessing GNSS correction signals for assisted guidance systems in agricultural vehicles. Precision Agriculture, 12, 639-652.
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