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Comparison of positional accuracy between RTK and RTX GNSS gased on the autonomous agricultural vehicles under field conditions

Applied Engineering in Agriculture
Vol. 30(3): 361-366 © 2014 American Society of Agricultural and Biological Engineers ISSN 0883-8542 DOI 10.13031/aea.30.10342 361
J. Carballido, M. Perez-Ruiz, L. Emmi, J. Agüera
ABSTRACT. Currently, many systems (machine vision, high resolution remote sensing, global positioning systems, and
odometry techniques) have been integrated into agricultural equipment to increase the efficiency, productivity, and safety
of the individual in all field activities. This study focused upon assessing a satellite-based localization solution used in
straight path guidance of an autonomous vehicle developed for agricultural applications. The autonomous agricultural
vehicle was designed and constructed under RHEA (Robot fleets for highly effective agriculture and forestry management)
project and is part of a three-unit fleet of similar vehicles. Static tests showed that 99% of all positions are placed within a
circle with a 2.9 cm radius centered at the geo-position using real-time satellite corrections (RTX). Dynamic tests between
rows demonstrated a mean (N=610) of the standard deviation for real-time base station corrections (RTK) of 1.43 cm and
for real-time satellite corrections (RTX) of 2.55 cm. These results demonstrate that the tractor was able to track each
straight line with high degree of accuracy. The integration of a Global Navigation Satellite System (GNSS) with sensors
(e.g., inertial sensor, altimeters, odometers, etc.) within the vehicle showed the potential of autonomous tractors for
expanding agricultural applications utilizing this technology.
Keywords. Autonomous tractor, GNSS, Precision agriculture, RTK-GPS, Agricultural machinery.
nnovative technologies (i.e., GNSS, GIS, machine
vision, sensors, agricultural machinery controller, and
high resolution remote sensing) are beginning to play a
vital role in agroforestry systems, as they aid in
compliance with current regulations while improving system
cost and efficiency (Fountas et al., 2006; Sørensen and
Bochtis, 2010). In recent years, several studies have emerged
suggesting that fleet of automated agricultural machinery can
increase sustainability and competitiveness in agricultural
production (Blackmore et al., 2005; Peleg, 2005; Bakker et
al., 2011). There are, however, important challenges that
must be overcome in these fleet automated systems. These
challenges include lowering the control system cost,
increasing production flexibility, and reducing the number of
devices aboard each fleet to avoid the failure of one vehicle
causing the entire fleet to be out of order. Meanwhile, new
systems must also have affordable automation systems and
comply with health and safety regulations.
The placement of fleet automated technology in the
agroforestry sector may provide a number of benefits,
including 1) reducing environmental contamination from
excessive agrochemical applications by adopting Global
Navigation Satellite System (GNSS) based site-specific
application techniques, 2) increasing yields by optimizing
site-specific input application levels and 3) decreasing
necessity of skilled farm laborers required to perform
agricultural tasks.
An autonomous agricultural vehicle requires a combina-
tion of several techniques (sensors, machine vision
techniques, etc.) including GNSS. For real-time
applications that require on-the-go corrections, a
differential GNSS technique (DGNSS) is preferred to
achieve very high location accuracy. As the resolution at
which the geoposition improves, it increases the number of
plant-specific management tasks suited for automation. A
straightforward method to achieve accurate geopositioning
is to use two GNSS receivers (a rover and a base) that track
the same satellites. In this case, the position of the base (a
stationary unit) can be accurately determined using satellite
signals. The location information from the base can be used
to correct the location of the rover, and this correction
information can be communicated to the field GNSS
receiver by a radio link (Heraud and Lange, 2009; Perez-
Ruiz and Upadhyaya, 2012). This method allows for
minimization of error and higher real-time accuracy (Leica
Geosystems AG, 1999).
In today’s agricultural processes, RTK-DGNSS (Real
Time Kinematic-Differential GNSS) based auto steering
provides substantial savings in agro-chemicals and reduced
Submitted for review in July 2013 as manuscript number PM 10342;
approved for publication by the Power & Machinery Division of ASABE
in April 2014.
The authors are Jacob Carballido del Rey, PhD. Student, and Juan
Agüera Vega, ASABE Member, Professor, Department of Rural
Engineering, ETSI Agrónomos y Montes, University of Córdoba, Spain;
Manuel Pérez-Ruiz, Professor, Department of Aerospace Engineering
and Fluid Mechanics, Agro-forest Engineering, University of Seville,
Spain; Luis Emmi, PhD. Student, Center for Automation and Robotic
(UPM-CSIC), Madrid, Spain. Corresponding author: Manuel Pérez-
Ruiz, Department of Aerospace Engineering and Fluid Mechanics, Agro-
forest Engineering, University of Seville, Ctra. Sevilla-Utrera km 1,
Seville 41013, Spain; phone: +34-955481389; e-mail:
hand-weeding requirements, with the associated
environmental and economic advantages (Griepentrog
et al., 2004; Blackmore et al., 2005; Fennimore et al.,
2010). Although the use of two GNSS receivers requires a
significant financial investment, RTK-GNSS systems are
becoming increasingly common among commercial
farming operations for automatic steering of tractors and
other types of field equipment.
One disadvantage of using RTK-GNSS solutions in
agriculture is the requirement that a base station be located
within 10 km at all times, and this results in high capital
cost. Multiple reference station RTK trials have been on-
going since the late 1990’s (Hu et al., 2003; Ong Kim Sun
and Gibbings, 2005). For example, both Leica Geo-systems
and Trimble have provided such Network RTK services for
the whole Great Britain since early 2006 (Edwards et al.,
2010). Likewise, some government institutions are working
to mitigating this challenge by developing a network of
base stations, which provide access to the RTK correction
signal over a wide geographic region via cellular or radio
modem (Mesas and Torrecillas, 2007). In the future, this
network may provide coverage to all farmers with RTK-
GNNS receivers, eliminating the need for multiple base
stations on each farm. However, another factor that must be
considered, due to the increased use of GNSS base stations,
is the lack of knowledge as to how the base station
coordinates are influenced by the movement of tectonic
plates (Prawirodirdjo and Bock, 2004).
Recently, a real-time positioning products has been
released (i.e., RTX), claiming to bridge the gap between
real-time RTK-PPP (Real Time Kinematic-Precise Point
Positioning) and Network RTK-GNSS. These develop-
ments are a combination of real-time data and innovative
positioning algorithms to deliver centimeter accuracy
around the world and allow satellite correction to be
delivered directly to the GNSS rover receiver, with no need
for additional equipment such as radios and antennas. Rizos
et al. (2012) reported that RTX is capable of providing real-
time positioning at 4 cm level horizontally (95%), with
initialization times of less than 1 min.
The aim of this study was to determine the GNSS
centimeter-level accuracy, through RTK (from base station)
and RTX (from satellite) signals, of the straight path
provided for an autonomous vehicle developed for
agricultural applications.
Real-Time Differential GNSS Correction
With 2 cm accuracy, RTK systems are the most accurate
solution for GNSS (Global Navigation Satellite System)
applications. An RTK system requires two receivers, a
radio link, and an embedded navigation controller that
integrates rover sensors and GNSS data to compute the
final position of the rover receiver (Misra and Enge, 2006).
In this study, an RTK-GNSS receiver (BX982, Trimble
Navigation Ltd., Sunnyvale, Calif.) was used to accurately
locate the autonomous tractor for all field trials. The
GNSS-based navigation system included:
a rover RTK-GNSS receiver with two GPS antennas
mounted on top of the tractor’s cabin 2 m above the
soil surface and 1.5 m apart,
vehicle steering actuators,
manual override sensors,
steering angle sensors,
controller that implement steering correction
algorithms, and
terrain compensation sensing (i.e., pitch, roll, and
The system utilized an RTK-GNSS correction signal
from a local (located ~0.3 km from the test site) GNSS base
station (Trimble Model BX982) to obtain RTK Fixed
quality accuracy. The rover was set to output the
“NMEA-0183 PTNL, AVR” string containing the
geographic coordinates (latitude and longitude) and yaw
angle in degree and range (m) between primary and
secondary antennas at 1 Hz rate via an RS-232 serial
Real-Time Extended GNSS Correction
The real-time extended (RTX) positioning is a new
technology that provides users with centimeter-level real-
time position accuracy. The correction signal is based on
satellite information generated at processing centers and
broadcast to users through satellites. Horizontal position
error obtained in real-time, via a receiver acquiring the
RTX correction data through the satellite link in North
America (Ames, Iowa), was RMS 1.4 cm, with a 95%
horizontal error of 2.4 cm (Leandro et al., 2011). Using an
RTX signal is advantageous because it does not require a
local base station for signal correction.
The autonomous agricultural tractor was designed and
constructed under a European research project and is part
of a three-unit fleet of similar vehicles (RHEA, 2012). The
platform of the autonomous vehicle was a conventional
38 kW tractor (New Holland model Boomer T3050, 3-point
hitch, Zedelgem, Belgium) that was retrofitted for
autonomous agricultural operations. Figure 1 shows the
Figure 1. Autonomous tractor unit configuration.
30(3): 361-366 363
equipment setup used in the field experiments, and figure 2
shows how the GNSS correction signals were captured and
transmitted to the receiver through an external port.
A specially fabricated frame was located in the retrofit-
ted tractor and used to mount the most necessary
equipment, including the on-board computers, inertial
measurement unit, modems for navigation, connector
boxes, etc. The motion of the autonomous tractor had three
primary degrees of freedom (longitudinal, lateral, and yaw).
The tractor controller was responsible for sensing the
vehicle location and heading angle.
To configure a fully autonomous agricultural system
capable of ensuring precise navigation (navigation system),
it is necessary to configure a framework (hardware and
software) to merge perception (accurate vehicle
positioning) and action (steering and speed control). The
hardware framework should be modular, flexible, and
robust, exhibiting real-time multitasking features and
integrating modern standard communication protocols.
Specifically, the vehicle controller used in this part of the
experiment was based on a cRIO 9082 NI computer, and
the control algorithms were developed using the LabVIEW
graphical programming environment (Emmi and Gonzalez-
De-Santos, 2012).
Field tests were performed over a 1 week period during
the winter of 2013 at the Center of Automatic and Robotic
field experiment site, at the Spanish National Research
Council (CISC), Madrid (latitude: 38.53894946 N,
longitude: 121.7751468 W). Three criteria used for
choosing the test plot were the following: (i) a plot that was
almost flat, (ii) a plot large enough for five 20 m rows, and
(iii) a plot that was within range of the correction base
station used in the experiment.
A static test was carried out on a building of approxi-
mately 20 m in length where an open sky was visible. In
this first test the RTX calculation was performed for the
rover receiver and provided an accurate position of the new
European correction signal using a GNSS navigation
receiver. The correction signal was tested for 30 minutes on
three different days, at different times of the day, within the
same week as a dynamic test. Based on manufacturer
recommendations (Lemmon and Wetherbee, 2005), this
testing procedure would provide enough satellite
constellation averaging to estimate the GNSS system
Each dynamic test consisted of five passes of 20 m
following a straight line (fig. 3). Two points (“AB”) for
each straight line were generated as an actual geospatial
location by an RTK-GNSS receiver using a handheld
surveying system interfaced to a rover RTK-GPS (Trimble
model Bx982). The geographic coordinates for points “A”
and “B” were obtained by placing the bottom tip of the 2 m
GPS antenna survey pole against the soil surface and
holding the pole vertically with the aid of a bubble level.
Points A and B were established for a dual-purpose: a)
the straight-line mission planning for the autonomous
tractor and b) a straight-line marked on the ground for
accuracy measurements. All passes were travelled at a
travel speed of 2.5 km h-1.
Figure 4 shows the small tillage steel piece that was
attached under the autonomous tractor, in the central axis,
to mark the ground with the actual path of the autonomous
In this experiment, two types of GNSS correction
signals were used: (1) RTK-GNSS signal provided by the
base station and (2) RTX-GNSS based on satellite
correction trough a satellite link.
The following raw GNSS data were recorded for all the
dynamic tests on the autonomous tractor: UTC time,
longitude, latitude, height, velocity, signal quality indicator,
PDOP, heading, and number of satellites. Only the time,
longitude, latitude, and heading were utilized for the
accuracy analysis. A program was created in LabVIEW
(National Instruments, Austin, Tex.) to convert geographic
coordinates to UTM coordinates.
To determine the accuracy of the autonomous tractor
path compared to the prescribed path, the single point
cross-track error (XTE) was defined as the perpendicular
distance from the straight-line “AB” to each error
measurements on the ground. Measurements were taken
every 0.2 m between the ideal straight-line and the
autonomous tractor path.
Total XTE was calculated using the root mean squared
(RMS) value of all the single point XTEs along the full
length of the straight-line (Taylor and Schrock, 2003).
Cross-track error is an important variable that affects the
potential skip or overlap.
For the t th pass, the RMS error was then calculated with
the following equation:
Nt = total number of measurement point for the t th pass,
eit = distance from the point i to the t th pass.
For the statistical analysis, the errors were calculated for
each measurement. The SAS general linear models
Figure 2. Flowchart of the location system on autonomous tractor.
364 A
procedure (SAS, 2008) was used to test for significant
differences between both treatments (RTK vs. RTX) using
ANOVA. Statistics for the GNSS receiver (RTX satellite
correction) position accuracy values in static tests were
calculated using JMP (SAS Institute, Cary, N.C.).
In total, 4970 GNSS data points were logged on three
different days in the same week: day 1 (1220 data points),
day 2 (1800 data points), and day 3 (1950 data points).
Figure 5 shows the visibility of the GNSS satellite during
test day 2 (10 GPS + 7 GLONASS); these conditions were
similar to other static and dynamic test days. Table 1 shows
the mean, standard deviation, maximum, minimum, and
RMS values for the GNSS receiver error when using the
RTX correction signals. The small RMS error for this test
with RTX correction indicates that RTX has the potential to
be used in an autonomous tractor. The magnitudes of the
average circular error probable (CEP) was 2.9 cm at 99%,
Figure 3. Straight mission for the autonomous tractor.
Figure 4. Implemented steel tillage bar on the autonomous tractor.
Figure 5. Plot of the visible GNSS satellites.
30(3): 361-366 365
which means that 99% of all positions are placed within a
circle with a 2.9 cm radius centered at a real position.
The GNSS antennas mounting location on the autono-
mous tractor enabled an unobstructed view of the sky
during the entire trial. This allowed for optimal signal
reception regardless of satellite geometry, and the RTK and
RTX-GNSS fixed quality was obtained for the recording of
all data. The data in table 2 shows the RMS and standard
deviation values for the GNSS receiver error mounted on
the autonomous tractor when using RTK and RTX-GNSS
correction signals. The rover receiver had a 2.5 cm
horizontal accuracy and a 3.7 cm vertical accuracy on a
continuous real-time basis. This level of accuracy was
expected because the RTK technique can determine the
sensor position within a few centimeters (Trimble, 2007).
The average 2.4 cm for RMS cross-track error in the RTK
correction signal system indicates that the passes were very
straight, as was desired for the autonomous tractor. The
9.8 cm average error in the RTX correction signal system
could limit the use of RTX-based autonomous tractor
application in some horticultural crops and agricultural
operations that require a high degree of accuracy. This
unfortunate RTX accuracy was coincident with a
significantly large heading error. However, fully automatic
vehicles could be used for automated precision farming in
many other applications such as site-specific management
of weed control on extensive crops, variable rate
application in orchards and vineyards using the appropriate
implement, and variable-rate application of fertilizer based
on yield maps. Between rows there was an error with a
constant standard deviation, the average of these for RTK
was 1.43 cm and for RTX 2.55 cm. These results
demonstrate that the tractor controller was able to track
each straight line with a standard deviation of better than
3.5 cm; the vehicle lateral position error never deviated by
more than 4 cm for RTK and 10 cm for RTX.
There is a base of scientific research focused on
achieving accurate geopositioning information through
RTK-GNSS equipment mounted on an autonomous tractor
using a dedicated reference station for signal correction
(e.g., Nørremark et al., 2007; Sun et al., 2010; Griepentrog
et al., 2005). To the best of our knowledge, however, an
autonomous tractor using a DGNSS system has not been
fully implemented. This study demonstrated the feasibility
using a real-time RTX based on GNSS correction signal
from an autonomous tractor where extreme accuracy is not
required. The following conclusions were drawn based
upon the results of this research:
RMS Easting and Northing for the static tests with
RTX correction showed values of 0.90 and 1.13 cm,
respectively. This indicates that RTX has the poten-
tial to be used to get the location of autonomous
tractors for applications that require a high degree of
The RMS error of the autonomous tractor using the
base station (RTK-GNSS) signals was approximately
four times less than the RMS using the RTX correc-
tion signals. However, a fully automatic vehicle
could be used for automated precision farming in
many applications where a very high level of accura-
cy is not required, such as, site-specific management
of weed control on extensive crops, variable rate
application in orchards and vineyards using the ap-
propriate implement, and variable-rate fertilizer
application based on yield maps.
The study has shown that the real-time extended
GNSS signal could be used on an autonomous trac-
tor, which greatly reducing the total equipment cost
of the system without a large performance penalty.
This research was funded by the European Union’s
Seventh Framework Program [FP7/2007-2013], under
Grant Agreement 245986 in the Theme NMP-2009-3.4-1
(Automation and robotic for sustainable crop and forestry
management). The authors thank Alberto Jarduo, Pablo
Agüera, and Soluciones Agrícolas de Precision S.L in
Cordoba/Sevilla, Spain for their technical assistance.
Table 1. Statistics for GNSS receiver using RTX correction signal on static, i.e. the autonomous tractor without motion.
Statistics for RTX Position Accuracy Values
Easting (cm) Northing (cm)
Day GNSS Data Mean S.D. RMS Max. Min. Mean S.D. RMS Max. Min.
1 1220 0.00 0.80 0.80 2.10 -2.50 0.00 0.90 0.90 2.80 -2.30
2 1800 0.00 1.00 1.00 2.80 -3.60 0.10 1.00 1.00 3.70 -3.30
3 1950 0.00 0.92 0.90 2.50 -3.20 0.10 1.00 1.50 3.20 -3.50
All data 4970 0.00 0.91 0.90 2.47 -3.10 0.07 0.97 1.13 3.23 -3.03
Table 2. Statistics for the GNSS receiver using the RTK and RTX correction signals on the autonomous tractor with motion.
Row Measurements RMS S.D. RMS S.D.
1 122 2.71 2.10 7.89 2.94
2 122 3.36 1.52 7.02 3.44
3 122 1.36 0.87 11.73 2.13
4 122 2.93 1.61 9.91 2.88
5 122 1.65 1.05 12.73 1.34
All Rows 610.00 2.40 1.43 9.86 2.55
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... Utilizing an RTX signal is more useful because it was no more worrying about losing the signal of radio connection since a base station is not necessary while providing the productivity of RTK positioning. [4] HOW DOES RTX WORKS Trimble RTX technology uses data from a global reference station network distribution consists of more hundred stations, distributed across the globe. These data transmitted by internet to the RTK control center, which contains three parts: 1) Communication servers that utilities to delay network observation data to the data processing servers. ...
Full-text available
Real Time Extended (RTX) technology works to take advantage of real-time data comes from the global network of tracking stations together with inventor locating and compression algorithms to calculate and relaying the orbit of satellite, satellite atomic clock, and any other systems corrections to the receivers, which lead to real-time correction with high accuracy. These corrections will be transferred to the receiver antenna by satellite (where coverage is available) and by IP (Internet Protocol) for the rest of world to provide the accurate location on the screen of smartphone or tablet by using specific software. The purpose of this study was to assess the accuracy of Global Navigation Satellite System (GNSS) low-cost external antenna and possibility for using it with a smartphone to measure the points in Real Time Kinematic (RTK) and (RTX) modes, obtaining the same accuracy by using high-cost (GNSS) receiver with same modes. The assessment has applied through comparing the control points measured in static mode (3 to 5 hours) and corrected by Online Positioning User Service (OPUS) web-based processing software with same control points measured in RTX mode by GNSS low-cost external antenna (5 minutes). The results of an assessment were obtained horizontal and vertical location error in real time, by receiver getting the RTX correction data over the satellite link were RMS (east 41cm, north 35 cm, elevation 94 cm), that means it’s more suitable for automotive, agriculture, and forestry application, As for the RTK mode, the comparison of the differences in RTK mode between the two antennas were RMS (north 5 cm, east 6 cm, elevation 10). This result indicates that the GNSS low-cost external antenna might be very useful in accurate surveying application.
Technical Report
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The first commercial GPS Real-time Kinematic (RTK) positioning products were released in 1993. Since then RTK technology has found its way into a wide variety of application areas and markets including Survey, Machine Control, and Precision Farming. Current RTK systems provide cm-accurate positioning typically with initialization times of seconds. However, one of the main limitations of RTK positioning is the need of having nearby infra-structure. This infra-structure normally includes a single base station and radio link, or in the case of network RTK, several reference stations with internet connections, a central processing center and communication links to users. In single-base, or network RTK, the distances between reference stations and the rover receiver are typically limited to 100 km. During the last decade several researchers have advocated Precise Point Positioning (PPP) techniques as an alternative to reference station-based RTK. With the PPP technique the GNSS positioning is performed using precise satellite orbit and clock information, rather than corrections from one or more reference stations. The published PPP solutions typically provide position accuracies of better than 10 cm horizontally. The major drawback of PPP techniques is the relatively slow convergence time required to achieve kinematic position accuracies of 10 cm or better. PPP convergence times are typically on the order of several tens of minutes, but occasionally the convergence may take a couple of hours depending on satellite geometry and prevailing atmospheric conditions. Long initialization time is a limiting factor in considering PPP as a practical solution for positioning systems that rely on productivity and availability. Nevertheless, PPP techniques are very appealing from a ground infrastructure and operational coverage area perspective, since precise positioning could be potentially performed in any place where satellite correction data is available. For several years, efforts have been made by numerous organizations in attempting to improve the productivity of PPP-like solutions. Simultaneously, efforts have been made to improve network RTK performance with sparsely located reference stations. Until now there has not been a workable solution for either approach. Commercial success of the published PPP solutions for high-accuracy applications has been limited by the low productivity compared to established RTK methods. In this paper we present a technology that brings together the advantages of both types of solutions, i.e., positioning techniques that do not require local reference stations while providing the productivity of RTK positioning. This means coupling the high productivity and accuracy of reference station-based RTK systems with the extended coverage area of solutions based on global satellite corrections. The outcome of this new technology is the positioning service CENTERPOINT RTX™, which provides real-time cm-level accuracy without the direct use of a reference station infrastructure, that is suitable for many GNSS market segments. Furthermore, the RTX solution is applicable to multi-GNSS constellations. The new technology involves innovations in RTK network processing, as well as advancements in the rover RTK positioning algorithms.
Conference Paper
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The combination of seed mapping technology and computer vision based recognition and identification of seedlings will provide the basis for improved accuracy and efficiency of the described weed control system. This combined technology together with integrated mechanical and micro-spray intra-row and close-to-crop weed control will represent a new unique way of a weed management strategy and will result in a significant synergetic increase of weeding efficiency. Farmers could utilize this technology to reduce input of chemicals and to be more efficient in crop production, e.g. in sugar beets, maize, vegetables and other high value crops.
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In response to the recent growth in multiple reference station networks throughout the world, a pilot project of the Virtual Reference Station (VRS) network has been established in south‐east Queensland. Independent testing of this network was required to establish its performance in post processed and real‐time positioning, and its reliable coverage area.Tests were conducted at several sites, both inside and outside the network. GPS data from a single antenna was used to simultaneously record realtime positions derived from both the VRS base stations and a conventional base station. This data was analysed in terms of accuracy precision and initialisation times. At the same time, raw data was logged for later analysis of the post processing capabilities of the VRS.Accuracy and precision estimates from the data collected showed that the VRS is at least comparable to, and in some instances may be considered superior to, conventional RTK. For example, during tests when low numbers of satellites were visible, the VRS‐RTK was able to initialise in shorter times than conventional RTK.In general, the VRS‐RTK proved to be a reliable substitute for conventional RTK using a single base station. In fact, VRS‐RTK was shown to be more reliable and robust than conventional RTK, and in many instances was able to produce results where conventional RTK failed. VRS also showed great potential for post processing that, until now, has been largely ignored.
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Machine-vision cultivator guidance systems are commercially available to growers, but little work has been done to determine if these guidance systems can improve integrated weed management systems in vegetable crops. Studies were conducted in 2005 and 2006 in broccoli and lettuce to evaluate band-applied DCPA or pronamide, respectively, and four noncultivated bands ranging from 5.1 to 12.7 cm. DCPA or pronamide were applied in bands centered on the seed line at 0, 7.6 or 12.7 cm wide. A commercial machine-vision system was used to guide a commercial cultivator. Generally, weed densities and hand-weeding times were less where the DCPA band in broccoli or the pronamide band in lettuce were 7.6 or 12.7 cm wide compared to no herbicide. Weed densities were lowest in both crops where the noncultivated band width was 5.1 cm compared to 12.7-cm noncultivated bands. For broccoli in both 2005 and 2006, net returns above production costs were generally higher in the 7.6- and 12.7-cm-wide DCPA bands compared with the no-herbicide band. In lettuce in both years, the no-pronamide treatment had higher net returns, when compared with the 7.6- and 12.7-cm pronamide bands. Lettuce yields and higher net returns in the no-pronamide treatment compared to the 7.6- and 12.7-cm pronamide bands may be due to slight yield reduction from pronamide. Results suggest that pronamide was not needed during the dry months of the year when weed management tools such as hand-weeding and cultivation work very well. However, in periods of rainy weather when cultivation and hand-weeding are not possible, then pronamide would likely provide the greatest economic benefit. Given the large impact of cultivation oil vegetable weed management programs, the greatest potential benefit of machine-vision guided cultivators is if they facilitate more timely and effective cultivation.
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As of March 2009, network real-time kinematic (RTK) GPS surveying is available in Great Britain with the aid of two commercial service providers, Leica's "SmartNet and Trimble 's "VRS Now", both of which rely largely on the Ordnance Survey's "OS Net" network of around 120 continuously operating reference stations. With the aim of testing the performance of Network RTK under both ideal and less-ideal conditions (greater distances and elevation differences from the nearest reference stations, proximity to the edges of OS Net, and increased susceptibility to ocean tide loading effects), we hove tested the positional accuracy of both commercial Network RTK systems by comparison with precise coordinates determined using the Bernese scientific GPS processing software, at six representative locations spanning England and Wales. We find that the coordinate quality measures provided by the Network RTK solutions are overall representative of the actual coordinate accuracy, which is typically 10-20 mm in plan and 15-35 mm in height, and can be successfully used to identify outliers. Positional accuracy tends to be poorest outside of the bounds of OS Net and at greater elevation differences from nearby reference stations. Averaging of coordinates over two short windows separated by 20-45 minutes can be used to achieve moderate improvements in coordinate accuracy without the need for single long occupations of sites.
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Individual plant care may well become embodied in precision farming in the future and will lead to new opportunities in agricultural crop management. The objective of this project was to develop and evaluate a data logging system attached to a precision seeder to enable high accuracy seed position mapping of a field of sugar beet. A Real Time Kinematic Global Positioning System (RTK GPS), optical seed detectors and a data logging system were retrofitted on to a precision seeder to map the seeds as they were planted. The average error between the seed map and the actual plant map was about 16–43 mm depending on vehicle speed and seed spacing. The results showed that the overall accuracy of the estimated plant positions was acceptable for the guidance of vehicles and implements as well as potential individual plant treatments.
We estimate a global plate motion model for 17 major and minor tectonic plates solely on the basis of analysis of data from 106 globally distributed continuous GPS stations, spanning the period from January 1991 to July 2003. Site positions estimated from 24-hour segments of data are aligned day-by-day to the International GPS Service (IGS) realization of the ITRF2000 reference frame using a similarity transformation, thereby ensuring that the ITRF2000 no-net-rotation condition is preserved. Linear velocities of a carefully selected set of stations are estimated from the position time series, along with annual and semiannual fluctuations and position offsets due to GPS instrument changes. A white noise plus flicker noise model is applied to estimate realistic uncertainties for the site velocities, which are then propagated into the derived plate motion model parameters. We also examine the vertical velocities in the site selection process. At the Scripps Orbit and Permanent Array Center we have implemented a procedure whereby the plate motion model is updated on a regular (monthly) basis to improve its precision and reliability as new data become available and as a baseline against which anomalous motions can be detected.
An RTK-DGPS (Real Time Kinematic Differential Global Positioning System) based autonomous field navigation system including automated headland turns was developed to provide a method for crop row mapping combining machine vision, and to evaluate the benefits of a behaviour based reactive layer in a hybrid deliberate systems architecture. Two experiments were performed at the same time: following of pre-planned paths reconstructed from crop row positions based on RTK-DGPS and crop row mapping by combining vision-based row detection with RTK-DGPS information. The standard deviation, mean, minimum and maximum lateral error of the robot vehicle while following a straight path on the field with RTK-DGPS at a speed of 0.3 m s
Efficient use of automatic field operations will allow care and management of crops in very different systems from what is known today and may lead to the possibility of individual plant care systems. Automatic field operation systems have the potential to reduce environmental impact while preserving economics in crop production. These systems require accurate and reliable information about the position of individual crop plants and, if possible, additional information about crop growth status. The aim of the presented research was to generate a geo-spatial map of individual crop plants derived from geo-referenced data recorded during seeding operation. A standard sugar beet precision seeder was retrofitted with optical sensors that could detect seeds as they were released into the furrow. Furthermore, a real time kinematic global positioning system (RTK-GPS) and a dual axis tilt sensor provided the global position and attitude angles of the seeder. A data acquisition system was configured for recording and storage of global positions, seeder attitude data, and seed drop detections during seeding. This paper outlines the methodology of processing the recorded data into a geo-spatial seed map. The developed instrumentation was used in field experiments under typical conditions and operation velocities up to 5.3 km h−1. The validation showed that 95% of the sugar beet seedlings emerged within 37.3 mm from the seed drop positions contained in the geo-spatial seed map. An error analysis associated with the estimation of geo-referenced plant positions consisted of positioning sensor errors, seed displacement in the furrow and deviation between location of plant emergence and the corresponding seed location. Furthermore, the error contribution from individual sources was of the same magnitude, except for the error due to deviation between location of plant emergence and the corresponding seed location. As this fully random error will always occur, it was considered meaningless trying to further minimize the sensor errors. Inclusion of seeder attitude data in the data processing significantly improved the accuracy of the estimation of geo-referenced plant positions and therefore it was concluded that a dual axis tilt sensor should be a required part of the instrumentation. Furthermore, it was shown that high accuracy of the estimation of geo-referenced plant positions required a zero horizontal velocity of the seed released from the seeding mechanism. In general, the overall accuracy of the estimation of geo-referenced plant positions was satisfactory to allow subsequent individual plant scale operations.