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第 26 卷 第 4 期 2010 年 4月
农 业 工 程 学 报 Transactions of the CSAE
Vol.26 No.4 Apr. 2010
Integrated multi-sensor hardware system for soil information measurement Ma Ruijun1, 2, Michael Short3, Craig Lobsey3, Alex McBratney3, Brett Whelan3, Budiman Minasny3, Sun Guangyong4 (1. College of Engineering, South China Agricultural University, Guangzhou 510642, China; 2. Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou 510642, China; 3. Australian Centre for Precision Agriculture, Faculty of Agriculture, Food and Natural Resources, The University of Sydney, NSW 2006, Austrilia; 4. State Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University, Changsha 410082, China) Abstract: Soil plays an important role in crop growth and soil information is necessary for making crop management decisions. Traditional soil sampling techniques are time-consuming and costly, especially for large survey areas and laboratory analysis. Currently a variety of on-the-go soil sensor techniques are available that can provide high-resolution digital soil maps but these commercial sensors are generally used individually. This paper presented an integrated hardware system by multi soil sensors, including Gamma Ray Spectrometer, Geonics EM38, Geonics EM31, Veris 3100 and Veris pH, which could measure different soil parameters simultaneously and avoided the vehicle numerous trips into the field and minimize soil compaction. The complementary data for the soil sensors could further enhance data-based decision-making and potentially offer new possibilities for precision agriculture. Issues with the system that need further research in the future were also discussed in the paper. The system is appropriate for the measurements of soil parameters at a fine spatial scale for large areas. Key words: sensors, hardware, soils, precision agriculture doi：10.3969/j.issn.1002-6819.2010.04.026 CLC number: S237, TP212 Document code: A Article ID: 1002-6819(2010)-04-0156-06 Ma Ruijun, Michael Short, Craig Lobsey, et al. Integrated multi-sensor hardware system for soil information measurement[J]. Transactions of the CSAE, 2010, 26(4): 156－161. (in Chinese with English abstract)
0
Introduction
There are numerous on-the-go soil sensors available both commercially and at prototype stages of development. These sensors can be classified according to their methods of measuring soil parameters including: mechanical, electrical and electromagnetic, optical and radiometric, acoustic, pneumatic and electrochemical[1]. Soil properties are complex and affect each other, to study this complexity and variability researchers have started to focus on developing multiple proximal soil sensors to simultaneously measure different soil parameters and provide complementary information for soil parameters. Lammers et al[2] designed a new penetrometer combining a dielectric transducer with a force transducer that simultaneously measured the soil resistance and soil water content. This system compensates for the influence of soil water on the resistance measurement and additionally provides data on soil water distribution across the field. Following the development of the dual-sensor horizontal penetrometer, an electrical Received date: 2009-03-13 Revised date: 2010-03-11 Foundation item: Funded by Australia Research Council, Discovery Project on High Resolution Digital Soil Mapping. Biographies: Ma Ruijun (1970-), Male, Associate Professor, Doctor, College of Engineering, Senior member of the Chinese Society of Agricultural Engineering (E041200177S). South China Agricultural University, Guangzhou 510642, China. Email:
[email protected]
conductivity (EC) sensor with a 4-ring Wenner-array was incorporated into the cone of the horizontal penetrometer, the experimental results showed that the improved technique could provide more information for interpreting soil physical conditions at field-scale[3]. Wong et al[4] used an EM38 and a gamma radiometric sensor to infer soil properties and build a relationship between ECa (apparent electrical conductivity) and 40K content with the soil type. The dual-sensor method overcomes the weakness of the single-sensor data and has the potential in conjunction with site-directed soil sampling for estimating the spatial distribution at a high resolution in complex field situations without the need for expensive and extensive direct sampling and measurements. Taylor et al[5] conducted a soil survey with two soil sensors, an electromagnetic induction (EMI) sensor (Geonics EM38DD) and a gamma-radiometer (SAIC Exploranium GRS320) and found that the gamma-radiometer produced better prediction for topsoil clay content and topsoil CEC (Cation Exchange Capacity) than the EMI sensor, however, the EMI predicted clay content better in the subsoil. Combining the sensor output produced correlation for the topsoil data but not the subsoil. Neither sensor nor any combination of sensors produced good correlations of soil pH value and topsoil P content. De Benedetto et al[6] use GPR (ground penetrating radar) and EMI (Geonics EM38DD) to gather sub-surface feature data on stony soils and with a DGPS (differential global positioning system) recorded the position of each
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马瑞峻等：多土壤信息测量传感器的硬件系统集成设计
measurement. The combined interpretation from both sensor measurements were beneficial for determining soil properties and structure and improving estimations of those soil parameters of greater agronomic interest. The project proposes a way to build a system of integrating multiple proximal on-the-go soil sensors, gamma ray spectrometer, ground conductivity meters (Geonics EM38 and EM31), Veris Mobile Sensor Platform (Veris 3100 and pH) combined with a DGPS (Omnistar HP8300, that is capable of collecting elevation data), to log data on soil parameters simultaneously and for high resolution digital soil mapping. An integrated multi-sensor hardware system for soil information measurement is introduced in this paper.
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on-the-go sensing of apparent electrical conductivity (ECa) and soil pH value. The size of length, width and height is dividedly about 2 850 mm, 2 400 mm and 1 750 mm. The Veris MSP is towed behind the vehicle (Fig.2).
Introduction of individual sensor a. Veris 3100
1.1 Vehicle with DGPS and GR-320 Portable Gamma Ray Spectrometer The vehicle provides the mobility in the field. The size of length and width is about 5 350 mm and 1 850 mm. The width between the centers of two tires is 1 520 mm. The Ominstar HP8300 DGPS is set up on the top of the vehicle (Fig.1a). GR-320 Portable Gamma Ray Spectrometer is based on the principle of radiometrics and supplied with an internal 0.25µCi Cesium source installed in the base of the detector unit. The gamma detector is mounted in a steel box on the utility truck can slide from the tray to cover the ground at a height of about 1 m (Fig.1b).
b. Suspend mechanism
Fig.2 Veris 3100 and suspend mechanism
1.3
a. Omnistar DGPS
b. Gamma ray detector
Fig.1
1.2
Mounted position of Omnistar DGPS and gamma ray detector on the vehicle
Veris 3100 The Veris Mobile Sensor Platform (MSP) can provide
Geonics EM38 and EM31 Measuring ground conductivity is particularly useful for mapping variations of a number of soil properties including texture, salt and moisture content etc. The EM38 (Geonics Ltd., Ontario Canada) has proven to be useful for many near-surface applications. For large-area surveys, the EM38 can be towed in a sled behind a vehicle[7]. The EM38 is placed in a timber sled for measurement application in our set-up and kept upright (in the vertical dipole mode) or on its side (in the horizontal dipole mode) when EM38 works in the field. The dimensions of EM38 and timber sled are 106 cm ×15 cm ×3.6 cm, 1 670 mm ×210 mm ×276 mm, respectively. The box with EM38 together is towed by vehicle during the field measurements. The EM38 provides measurement of ground conductivity with effective exploration depth range 1.5 m in the vertical dipole mode, shown as Fig.3a, and 0.75 m in the horizontal dipole mode. The EM31 is capable of measuring soil apparent electrical conductivity (quad-phase) and magnetic susceptibility (in-phase) to an effective exploration depth of approximately 6 meters with a boom length 4m. The EM31 can map geological variations, groundwater contaminants or any subsurface feature associated with changes in ground conductivity (Fig.3b). 1.4 Parameters of sensors The proximal on-the-go soil sensors mentioned above
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can be used to measure different soil properties. The
parameters of sensors as shown in Table 1.
a. Geonics EM38
Fig.3
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b. Geonics EM31
Geonics EM38 on top of sled and Geonics EM31 in field Table 1
Parameters of sensors
Soil Sensors
Parameters
Measurement depth/cm
Date Acquisition
Support Software
GR-320
256 channels spectra: Total Content (TC) of Response, 40K, 238Ur, 232Th
0-45
Internal memory or RS232 data port
Exploranium Version 3V03
EC-Sh
0-30
Veris 3100
EC-Dp
0-90
EC and pH Instrument (and with a null modem serial cable)
Software Version MSP 1.02a
pH value EM38 EM31 OmniSTAR HP 3800 DGPS
Post- download to PC
ECa-h
0-75
ECa-v
0-150
ECa-quad (Quad-phase) MS-In(Magnetic Susceptibility In-phase) Position (Longitude, Latitude, Elevation, GPS Time) (accuracy 10 cm)
Generally 256 channels operation of GR-320 is normally used. Of course, the number of channels can be selected as 512. In the Operation Mode of GR-320, ASSAY is the one of four evaluations. This selection will display data at the end of a Sample Period as ROIs (regions of interest)[8]: ROI#! = TOTAL COUNT in ppm eU and Counts/minute ROI#2 = POTASSIUM in % and Counts/minute ROI#3 = URANIUM in ppm and Counts/minute ROI#4 = THORIUM in ppm and Counts/minute The GR-320 has an internal memory that can record data in different formats, ROI (region of interest) date or Spectrum data. A GPS (global positioning system) can be connected to the RS-232 port (Systems that contain the RMC (recommended minimum data for GPS) sentence may also be directly compatible.) and provides the location data in a latitude-longitude format for GR-320 while measuring data. After the measurement, the data geo-referenced with coordinates from the GPS can also be sent to an external data-logger (such as a laptop or PC) through the RS-232 data port. However, the external data-logger and GPS can not be connected with the instrument of RS-320 at the same time because the rear of the instrument only has one 10-pin connector for RS232 serial input/output. Therefore for on-the-go mapping the GR-320 data is logged onto a laptop and the data is geo-referenced with coordinates from the GPS also logged onto the laptop. The Veris instrument is designed to accept GPS input in NMEA (national marine electronics association) format
0-600
RS-232
EM38xp v1.01
RS-232
EM31xp v1.01
RS-232
via an RS-232 connector. Measured data is logged in the Veris instrument on a flash memory chip, and can be transferred to computer using either a diskette drive or continuously via the serial port OUTPUT with a null modem serial cable. Veris EC data are output as a 5 column ASCⅡ text file, such as longitude, latitude, EC shallow array (EC-Sh, mS/m, 0-30 cm), EC deep array (EC-Dp, mS/m, 0-90 cm) and DGPS elevation (m). 3 files of the Veris pH output data are created during pH data acquisition. The format of Extracted Soil pH file type (VPHEXXX.DAT) is output as 6 columns, such as longitude, latitude, pH value, time, altitude and speed (the vehicle was moving at the time the sample was taken) and also contains 5 columns of file statistics at its end offset from the normal data. Because it is the same port for pH value data input and the output of data for file transfer, so on-the-go pH value data can not be real time connected to PC during pH value measuring. It need post-download pH value data to PC[9-10]. So, it needs a solution for continuously logging the data on-the-go. Two modes of EM38 can be chosen to measure ECa of soil, the vertical dipole mode (ECa-v) or horizontal dipole mode (ECa-h). The data from EM38 can be connected to PC through RS-232 port[11]. The data from the EM31 can also be collected in real time (RT) collection by connecting a computer directly to the RS-232 output port on the front panel with an optional RS-232 interconnect cable[11].
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Integration of hardware system of multi sensors
The design of the integrated multi-sensor is shown below (Fig.4).
unit: mm 1. EM31 2. Vehicle 3. Data processor (in the cabin) 4. GPS 5. Gamma detector 6. Veris 3100 7. EM38
Fig.4
Schematic of the hardware system integrated by multi soil sensors
EM31 is mounted ahead of the vehicle 1m above the ground and about 1 940 mm in front of the vehicle. The boom orientation is perpendicular to the direction of the vehicle while surveying in the field. But EM38 position orientation is parallel with the direction of machine behind the Veris 3100 which is mounted at the end of the vehicle. The distance between the EM38 and Veris should be at least 2 m because of the high sensitivity of the EM38 to metallic objects. Therefore the EM38 is about 3 m away from the Veris 3100. The measurements using EM31 and EM38 at the same time in the field are shown as Fig.5a. Veris is configured with the vehicle as a rear suspension mounted model and the measurements in the field are shown as Fig.5b. OmniSTAR HP 3800 DGPS is used. So, the total length of the system integrated by above soil sensors is about 14.8 m. The maximum width of the system is 4 m, which is determined by the extended boom of EM31.
a. Simultaneously measurements using EM31 and EM38 in the field
Fig.5
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b. Measurement of Veris 3100 in the field
Measurement of different soil sensors in the field
Questions and works in the future
3.1
Data acquisition for multi sensors One important aspect is to develop a data logger (CPU) with enough ports and corresponding software that can acquire and process all the data from each of the proximal sensors and send to the PC because GR-320, Veris, EM31 and EM38 use different loggers and softwares and one PC has no enough ports to directly connect with all sensors at the same time (Fig.6). Additionally the CPU will need software that can geo-reference the incoming measurements from all the sensors with the GPS data. Since GPS information is usually available as serial data, while the output of field sensors consists of analog or pulse signals, special equipment is required to handle the sensing and positioning together. The aim of the research in future is to develop an efficient data logging system that can facilitate the combined acquisition of GPS data and multiple sensors information. For efficient field survey purposes, the system must be simple to configure and easy to operate.
Fig.6
Sketch of multi-sensor measurement connecting with PC
A possible format of data logging file type is logged as 15 columns at least during data acquisition, 4 columns for longitude, latitude, GPS time and altitude from GPS, 4 columns for ROI#1−#4 from Gamma Ray Spectrometer, 2 columns for EC-Quad (quad-phase) and MS-In (Magnetic Susceptibility, in-phase) from EM31, one column for ECa from EM38, 2 columns for EC-Sh and EC-Dp from Veris EC and 2 columns for Veris pH and the Vehicle Speed (Fig.7).
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Longitude
EC-Sh
Latitude
EC-Dp
pH
Altitude
GPS Time
ROI#1
ROI#3
ROI#4
EC-Quad
EC-In
ECa
Vehicle Speed
Fig.7
A format of data logging file type
Additionally the sensor position data need to be manipulated to allow for offsets due to different distances from the GPS that is located in the middle of the vehicle. At the same time, the problem of different sampling time with different sensors also needs to be considered in software. 3.2 Economic and application of the system The dimension of the hard system integrated by multi soil sensors is large. It is just suitable for large area measurements that can potentially save time and reduce the cost, especial avoiding the risk of soil compaction because of measurement vehicle multiple times passing through in the field. The economic and condition need to be considered when using the integration system. Other questions include how to analyze the data from multiple sensors and how to use the results from the integrated system for practical applications, in particular for the applications of site-specific crop management in precision agriculture. The objective(s) measured by the system must be defined based upon the project goals and available resources, e.g., manpower, funding, analytical capabilities, etc..
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ROI#2
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Conclusions
The system integrated by multi proximal soil sensors including GR-320, Veris 3100, Veris pH, EM31, EM38 and Omnistar HP8300 GPS, could simultaneously measure soil parameters such as mineralogy, EC of different depths, pH value and soon in the field. It has the potential to yield complementary information for better understanding variation of soil parameters that would further enhance data-based decision-making by researchers and farmers and offer new possibilities for precision agriculture. It also can save time and money and avoid the soil critical compaction due to vehicle multiple measurements into the field etc.. But at the same time, the problem of the data log need to be resolved in future and how to make use of the integrated system to resolve the practice problems also need to be thought about. Acknowledgements: We thank the support provided by China Scholarship Council for a visiting scholarship at the Australian Centre for Precision Agriculture, Faculty of Agriculture, Food and Natural Resources, The University of Sydney. Thanks are also extended to the Australian Research
Council, providing funds for a Discovery Project on High Resolution Digital Soil Mapping. [References] Adamchuk V I. Development of on-the-go soil sensor systems[C]//1st global workshop on high resolution digital soil sensing and mapping (Volume 1), Sydney, Australia, 2008. [2] Lammers P S, Sun Y, Ma D, et al. Combined sensor for soil resistance and water content[C]//1st global workshop on high resolution digital soil sensing and mapping (Volume 1). Sydney, Australia, 2008. [3] Zeng Q, Sun Y, Lammers P S, et al. Improvement of a dual-sensor horizontal penetrometer by incorporating an EC sensor[J]. Computers and Electronics in Agriculture, 2008, 64(2): 333－337. [4] Wong M T F, Oliver K W Y, Robertson M J. Use of EM38 and gamma-ray spectrometry as complementary sensors for high resolution soil property mapping[C]//1st global workshop on high resolution digital soil sensing and mapping (Volume 2), Sydney, Australia, 2008. [5] Taylor J, Short M, McBratney A, et al. Comparison of the ability of multiple soil sensors to predict soil properties in a Scottish potato production system[C]//1st global workshop on high resolution digital soil sensing and mapping (Volume 2), Sydney, Australia, 2008. [6] De Benedetto D, Castrignano A, Sollitto D, et al. Non-intrusive mapping of subsoil properties in agricultural field with DPR and EMI[C]//1st global workshop on high resolution digital soil sensing and mapping (Volume 1), Sydney, Australia, 2008. [7] Corwin D L, Lesch S M. Apparent soil electrical conductivity measurements in agriculture[J]. Computers and Electronics in Agriculture, 2005, 46(1/2/3): 11－43. [8] GR-320 Portable Gamma Ray Spectrometer Users Manual[K]. Exploranium GS Ltd., Mississauga, Canada, 2000. [9] Veris Mobile Sensor Platform pH Manager Operations Manual and Veris 3100 Soil EC Mapping System Operations Manual[K]. Veris Technologies, America. http://www. veristech.com/support/manuals.aspx [10] Soil EC 3100 and soli pH manager. http://www.veristech. com/products.aspx. [11] Conductivity meters and technical notes. http://www.geonics. com/html/conductivitymeters.html and http://www.geonics. com/html/technicalnotes.html. [1]
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多土壤信息测量传感器的硬件系统集成设计 马瑞峻1,2，Michael Short3，Craig Lobsey3，Alex McBratney3， Brett Whelan3，Budiman Minasny3，孙光永4 （1．华南农业大学工程学院，广州 510642； 2．华南农业大学南方农业机械与装备关键技术省部共建教育部重点实验室， 广州 510642； 3．澳大利亚精确农业中心，农业、食品与自然资源学院，悉尼大学，新南威尔士 2006； 4．湖南大学汽车车身先进设计制造国家重点实验室，长沙 410082） 摘
要：土壤在作物的生长过程中起到了重要的作用，所以对于基于决策的作物生产管理，土壤信息是必需的。传统的土壤取样获
取土壤信息技术耗时且成本高，尤其是对于大规模农田土壤信息测量。目前一些近地面的可连续测量的土壤信息传感器技术能够提 供高精度的数字土壤信息地图，然而这些商业化的技术成熟的传感器通常需要单独使用。该文提出了将 γ射线光谱仪 GR320、利用 电磁感应原理的 EM38 和 EM31 以及 Veris 3100 和 Veris pH 这些可在农田近地面连续测量的土壤特性测试传感仪器集成在一起同时 使用的方案，介绍了此集成系统的硬件设备和相关特性参数以及今后需要继续研究解决的问题。利用该系统可一次获得不同的土壤 特性参数数据，如土壤矿物质含量，不同深度的土壤电导率值和土壤 pH 值等，可避免多次测量车辆行走对土壤的压实。多传感器 数据之间的互相补充可以进一步提高且更有利于精确农业中基于土壤信息的决策规划。该系统适用于大面积农田土壤特性测量。 关键词：传感器，硬件，土壤，精确农业
