Evaluating the navigation performance of multi-information integration based on low-end inertial sensors for precision agriculture

The main objective of this research was to evaluate the navigation performance of multi-information integration based on a low-end inertial measurement unit (IMU) in precision agriculture by utilizing different auxiliary information (i.e., GNSS real-time kinematic (RTK), non-holonomic constraints (NHC) and dual antenna GNSS). A series of experiments with different operation scenes (e.g., open sky in wet and dry soils) were carried out for quantitative analysis. For the position drift error during a 20-s GNSS outage, the dual-antenna GNSS-assisted approach did not provide a reduction, and the NHC reduced the maximum error in the lateral and vertical directions by over 80% in the dry soil test, but only by approximately 30% in the wet soil test. The heading error with continuous GNSS assistance can be less than 0.03° and be reduced by more than 90% with the aid of dual-antenna GNSS. Additionally, the NHC reduced the heading error from 0.54° to 0.21° and from 0.34° to 0.25° in the dry and wet soil tests respectively. The results suggested that the multi-information integration improved the positioning and orientation reliability. Moreover, the lateral positioning accuracy required for the control of agriculture autonomous vehicles was achieved at approximately 3.0 mm with over a 60% accuracy improvement brought by the dual-antenna GNSS assistance. In contrast to the vulnerability of a single system, multi-information integration can provide comprehensive navigation information with higher reliability and lower costs. Hence, multi-information fusion will be a great opportunity for agriculture to meet the high-accuracy and high-reliability requirements of precision agriculture.

     

Evaluation of agronomic and economic benefits of using RTK-GPS-based auto-steer guidance systems for peanut digging operations

Increasing the peanut (Arachis hypogea L.) digger efficiency by accurate placement over the target rows could minimize damaged pods and yield losses. Producers have traditionally relied solely on tractor operator skills to harvest peanuts. However, as peanut production has shifted to new growing regions in the Southeast US, producers face difficulties digging peanuts under conventional and new management schemes. The present study aimed to: (i) determine the effect of row deviations (RD) of the digger from the target row on peanut yield and quality, and (ii) determine the economic value of using RTK auto-steer guidance systems to avoid tractor deviations during peanut harvest. The study consisted of a randomized complete block design of tillage [conventional (CT) and strip tillage (ST)], row patterns [single (SR) and twin (TWR)] and row deviation (RD_0 mm, RD_90 mm, and RD_180 mm). The RD_90 mm and RD_180 mm treatments exemplify manual driving deviations compared to using an RTK auto-steer guidance system (RD_0 mm). Higher yields and higher net returns resulted from using the RTK auto-steer guidance system. Data showed that for every 20 mm row deviation, an average of 186 kg ha^?1 yield loss can be expected. Overall, yield was higher for the conventional tillage and twin row pattern treatments compared to the other treatments. Yield losses for the SR-CT treatment were higher as the row deviation increased compared with the TWR-CT treatment. In contrast, higher yield losses for TWR-ST compared to SR-ST were observed when deviations of 180 mm occurred instead of digging using the RTK auto-steer guidance system. While a farmer using an RTK auto-steer guidance system with an accuracy within 25 mm (RD_0 mm treatment) could potentially expect additional net returns of between 94 and 404 $ ha^?1 compared to those from row deviations of 90 mm, higher net returns of between 323 and 695 $ ha^?1 could be perceived if the guidance system is used instead of having row deviations of 180 mm. Therefore, the use of RTK auto-steer guidance system will allow growers to capitalize on the increases in yield potential by implementing changes in tillage and row patterns as those evaluated in this study.     

Evaluating corrections for a horizontal offset between sensor and position data for surveys on land

When regarding evolutions in land-based, motorized sensor data collection it can be generalized that the speed of surveying, sampling rates, digital communications speed and survey resolutions have increased over the last years. Possibilities for accurate positioning have been following pace. However, a spatial offset between sensor and position data can be necessary to avoid interference with the instrument or can be the result of using a sensor array. It can also be due to practical considerations such as mounting possibilities. Unfortunately a severe degradation of positional accuracy is possible when performing corrections for a spatial offset and quantification of the induced error is quite difficult. As a consequence, the actual positional accuracy of sensor measurements is therefore often omitted or unknown, and a correction may be neglected during data processing. In this paper, accounting for a horizontal (spatial) offset is researched by examining the use of several correction methods. To evaluate the degree of loss of positional accuracy and validate several correction procedures, global navigation satellite system (GNSS) data (with real-time kinematic correction) have been simultaneously collected, using a GNSS receiver that was mounted on an all-terrain vehicle and two other receivers that were mounted near the front and end of an elongated sensor sled. The sled was connected to the towing vehicle using a flexible connection. Since the positioning systems’ horizontal accuracies were about 20 mm, it was possible to quantify the horizontal error of the predicted positions for the different correction procedures considered. The best approach for high-resolution surveys, which make use of a connection to a cart or sled that can rotate around a pivot on the towing vehicle, was researched. The strengths and weaknesses of the applied corrections were also evaluated, allowing selection of an appropriate correction for a given survey implementation.     

Short- and long-term dynamic accuracies determination of satellite-based positioning devices using a specially designed testing facility

Short-term dynamic accuracy is one of the most significant parameters for agricultural application of satellite-based positioning on straight segments. This is an important characteristic of guidance devices for every user. Two standards are proposed for dynamic testing of satellite-based positioning devices for utilization in agriculture. Previous approaches were typically based on the use of RTK records as a reference for error calculation. A test procedure based on a highly precise testing facility was developed, having a mechanical offset of the test cart less than 0.002 m. The new mathematical approach for straight line segment definition, based on two representative points was developed. This approach enables error-free calculation of off-track errors and determination of U-turn influence based on two additional parameters. Long-term testing was performed (24 h), using a Garmin GPS 18 device for test validation. The obtained results showed that this method can be successfully used for testing dynamic characteristics of GNSS devices. The results were as follows: mean value of the off-track errors was −0.39 m, standard deviation of all measured off-track errors was 1.51 m, being 1.3 m in 95% of the measured off-track errors. The pass-to-pass average error was 0.90 m, being under 0.662 m in 95% of the detected passes. The designed facility and developed procedure can also be successfully applied to the testing of high precision devices, e.g., RTK.     

Crop height variability detection in a single field by multi-temporal terrestrial laser scanning

Information on crop height, crop growth and biomass distribution is important for crop management and environmental modelling. For the determination of these parameters, terrestrial laser scanning in combination with real-time kinematic GPS (RTK–GPS) measurements was conducted in a multi-temporal approach in two consecutive years within a single field. Therefore, a time-of-flight laser scanner was mounted on a tripod. For georeferencing of the point clouds, all eight to nine positions of the laser scanner and several reflective targets were measured by RTK–GPS. The surveys were carried out three to four times during the growing periods of 2008 (sugar-beet) and 2009 (mainly winter barley). Crop surface models were established for every survey date with a horizontal resolution of 1 m, which can be used to derive maps of plant height and plant growth. The detected crop heights were consistent with observations from panoramic images and manual measurements (R² = 0.53, RMSE = 0.1 m). Topographic and soil parameters were used for statistical analysis of the detected variability of crop height and significant correlations were found. Regression analysis (R² < 0.31) emphasized the uncertainty of basic relations between the selected parameters and crop height variability within one field. Likewise, these patterns compared with the normalized difference vegetation index (NDVI) derived from satellite imagery show only minor significant correlations (r < 0.44).     

基于RTK差分GPS的平地机作业质量评估

为了取得平地机作业质量数据,评估RTK差分GPS平地机作业质量效果,对853农场约0.3 hm~2水田进行了搅浆平地试验。先使用了传统的农田作业质量评估方法来对其进行简单的初步评估,为了得到更加准确的评估数据,再使用了精确的农田质量评估方法。评估结果表明,使用RTK差分GPS平地机进行农田平地作业,能够满足农田平整精度要求。     

Centimetre-precision guidance of moving implements in the open field: a simulation based on GPS measurements

High quality and sustainable agricultural production in the open field will be supported by centimetre-precision guidance of agricultural implements. In this paper, a complete guidance system is proposed and simulated using real sensor data obtained from a real time kinematic (RTK) differential global positioning system (DGPS). This type of the satellite navigation system became available commercially in 1997 and is claimed to reach cm accuracy. It uses real time kinematics to improve the accuracy of a position fix by phase comparison of the carrier signal. The simulation primarily aims to investigate the accuracy of the guidance system that can be obtained with RTK DGPS. Using real DGPS measurement errors the simulated system showed a tracking error less than 0.005 m when the PID-controller had settled.     

GPS静态相对定位应用及精度研究

利用GPS静态相对定位,建立了云南省永善县土地二次调查中的控制网,并用常规RTK技术加密控制网以提高精度。对静态相对定位进行精度检测,证明静态相对定位误差范围小且稳定,上午观测能获得相对较高的精度,验证了研究区的控制网能满足精度要求。研究结果对GPS静态相对定位应用具有指导意义。     

Validation of GNSS under 500,000 V Direct Current (DC) transmission lines

The use of Global Navigation Satellite Systems (GNSS) is common among agricultural users and enables the producer to optimize crop production within soil variant fields to provide better farming practices. Many agricultural navigation systems are dependent on real time GNSS navigation solutions to aid and control farm machinery. Direct Current (DC) and Alternating Current (AC) transmission lines overhead are often suspected to create interference with GNSS equipment preventing farmers from utilizing their GNSS supported equipment. This paper provides evidence that only non-impeding effects on the receiver or incoming signals, in the form of cycle slips, were measured or detected from either the overhead lines and/or their corresponding support towers. No effect on code measurements was detected. The latter effect is due to reflection or brief masking by the towers. Tests were conducted under a set of three transmission lines, two 500 kV DC lines and one 230 kV AC line. Several GNSS receivers and processing methods, including real time and post-processed data, are used to measure and process data to study the position accuracy, dilution of precision, number of satellites tracked, code and phase errors, location and number of carrier phase cycle slips, carrier-to-noise density and L1–L2 carrier divergence. One commercial Real Time Kinematic (RTK) survey system was also used to verify the 450 MHz data link was operational.     

GPS接收机RTK定位中整周模糊度的快速解算方法

该文以单频GPS接收机为基础,以RTK(实时载波相位动态测量)为目标,通过丈量短基线、交换天线,以及前后载波相位观测值的处理,在2min内解算出具有足够精度和可靠性的整周模糊度.失锁时利用失锁前最近的定位点再次初始化,由此实现的差分GPS动态定位精度可达平面误差бP≤±5cm,高程误差бH≤±10cm.可广泛应用于工程测量、放样、测图,实例证明,在水土保持、林业资源调查、环境监测中亦有广泛的应用.     

Monitoring cotton (<Emphasis Type="Italic">Gossypium hirsutum</Emphasis> L.) germination using ultrahigh-resolution UAS images

Examination of seed germination rate is of great importance for growers early in the season to determine the necessity for replanting their fields. The objective of this study was to explore the potential of using unmanned aircraft system (UAS)-based visible-band images to monitor and quantify the cotton germination process. A light-weight UAS platform was used, which carried a consumer-grade red, green, and blue camera stabilized by a built-in gimbal system. In order to obtain ultrahigh image resolution during the germination stage, the UAS platform was flown at an altitude of approximately 15–20 m above ground. By applying the structure-from-motion (SfM) algorithm, the images were rectified and orthographically mosaicked with a ground sampling distance of approximately 6–9 mm/pixel. A novel solution was then developed for calculating the average plant size and the number of germinated cotton plants according to the leaf polygons extracted from the orthomosaic images. By using the estimated number of germinated cotton plants, the plant density and the cumulative germination rate can also be estimated in a straightforward manner using field-specific parameters. An assessment of the proposed solution was conducted by comparing the estimated number of the germinated cotton plants against ground observation data collected from six cotton row segments. The results demonstrated that the average estimation accuracy achieved 88.6% in terms of identifying the number of the germinated cotton plants. The accuracy may be further improved if images with near infrared band are employed.     

Unmanned aerial vehicle canopy reflectance data detects potassium deficiency and green peach aphid susceptibility in canola

There is growing evidence that potassium deficiency in crop plants increases their susceptibility to herbivorous arthropods. The ability to remotely detect potassium deficiency in plants would be advantageous in targeting arthropod sampling and spatially optimizing potassium fertilizer to reduce yield loss due to the arthropod infestations. Four potassium fertilizer regimes were established in field plots of canola, with soil and plant nutrient concentrations tested on three occasions: 69 (seedling), 96 (stem elongation), and 113 (early flowering) days after sowing (DAS). On these dates, unmanned aerial vehicle (UAV) multi-spectral images of each plot were acquired at 15 and 120 m above ground achieving spatial (pixel) resolutions of 8.1 and 65 mm, respectively. At 69 and 96 DAS, field plants were transported to a laboratory with controlled lighting and imaged with a 240-band (390–890 nm) hyperspectral camera. At 113 DAS, all plots had become naturally infested with green peach aphids (Hemiptera: Aphididae), and intensive aphid counts were conducted. Potassium deficiency caused significant: (1) increase in concentrations of nitrogen in youngest mature leaves, (2) increase in green peach aphid density, (3) decrease in vegetation cover, (4) decrease in normalized difference vegetation indices (NDVI) and decrease in canola seed yield. UAV imagery with 65 mm spatial resolution showed higher classification accuracy (72–100 %) than airborne imagery with 8 mm resolution (69–94 %), and bench top hyperspectral imagery acquired from field plants in laboratory conditions (78–88 %). When non-leaf pixels were removed from the UAV data, classification accuracies increased for 8 mm and 65 mm resolution images acquired 96 and 113 DAS. The study supports findings that UAV-acquired imagery has potential to identify regions containing nutrient deficiency and likely increased arthropod performance.     

RTK GPS mapping of transplanted row crops

This study demonstrated the feasibility of using a real-time kinematic (RTK) global positioning system (GPS) to automatically map the location of transplanted row crops. A positive-placement vegetable crop transplanter retrofitted with an RTK GPS receiver, plant, inclination, and odometry sensors, and an on-board real-time data logger were used for transplant mapping in the field during planting. Sensing the location where each plant was placed in the soil using an absolute shaft encoder mounted on the planting wheel proved to be more robust and accurate than using an infrared light beam sensor to detect the stem location of each plant immediately after planting. Field test results showed that the mean error between the plant map locations predicted by the planting data and the surveyed locations after planting was 2 cm, with 95% of the predicted plant locations being within 5.1 cm of their actual locations. Along-track errors were greater than transverse-track errors indicating that some improvement in plant map accuracy might be obtained by characterization of dynamic planting effects on final plant location. Overall, the system was capable of automatically producing a centimeter-level accuracy plant map suitable for use in precision plant care tasks such as intra-row weed control.     

实现网络RTK/RTD接收机的简易方法

介绍了1种可实现高精度定位的方法网络RTK。在分析JSCORS的差分信号资源列表的基础上,提出了一种简单、易行、便宜的实现网络RTK/RTD接收机的方法,并以成功接收网络RTD数据的过程来证明该方法的可行性。     

Geo-referencing remote images for precision agriculture using artificial terrestrial targets

The aim of this paper is to assess co-registration errors in remote imagery through the AUGEO system, which consists of geo-referenced coloured tarps acting as terrestrial targets (TT), captured in the imagery and semi-automatically recognised by AUGEO2.0^® software. This works as an add-on of ENVI^® for image co-registration. To validate AUGEO, TT were placed in the ground, and remote images from satellite Quick Bird (QB), airplanes and unmanned aerial vehicles (UAV) were taken at several locations in Andalusia (southern Spain) in 2008 and 2009. Any geo-referencing system tested showed some error in comparison with the Differential Global Positioning System (DGPS)-geo-referenced verification targets. Generally, the AUGEO system provided higher geo-referencing accuracy than the other systems tried. The root mean square errors (RMSE) from the panchromatic and multi-spectral QB images were around 8 and 9 m, respectively and, once co-registered by AUGEO, they were about 1.5 and 2.5 m, for the same images. Overlapping the QB-AUGEO-geo-referenced image and the National Geographic Information System (NGIS) produced a RMSE of 6.5 m, which is hardly acceptable for precision agriculture. The AUGEO system efficiently geo-referenced farm airborne images with a mean accuracy of about 0.5–1.5 m, and the UAV images showed a mean accuracy of 1.0–4.0 m. The geo-referencing accuracy of an image refers to its consistency despite changes in its spatial resolution. A higher number of TT used in the geo-referencing process leads to a lower obtained RMSE. For example, for an image of 80 ha, about 10 and 17 TT were needed to get a RMSE less than about 2 and 1 m. Similarly, with the same number of TT, accuracy was higher for smaller plots as compared to larger plots. Precision agriculture requires high spatial resolution images (i.e., <1.5 m pixel^?1), accurately geo-referenced (errors <1–2 m). With the current DGPS technology, satellite and airplane images hardly meet this geo-referencing requirement; consequently, additional co-registration effort is needed. This can be achieved using geo-referenced TT and AUGEO, mainly in areas where no notable hard points are available.     

GPS实时动态(RTK)技术在土地测量中的应用研究

GPS实时动态(RTK)测量技术是近年来应用的新技术,它与传统的测量技术相比,具有许多优点。结合浙江永康市土地测量的实践,探讨GPS实时动态(RTK)测量技术在土地管理中应用的控制网加密、参数转换、测量数据组织与编码等9个问题。实践表明,RTK测量可以较大幅度提高工作效率,尤其在通视困难地区更具明显优势。     

Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat

High spatial resolution images taken by unmanned aerial vehicles (UAVs) have been shown to have the potential for monitoring agronomic and environmental variables. However, it is necessary to capture a large number of overlapped images that must be mosaicked together to produce a single and accurate ortho-image (also called an ortho-mosaicked image) representing the entire area of work. Thus, ground control points (GCPs) must be acquired to ensure the accuracy of the mosaicking process. UAV ortho-mosaics are becoming an important tool for early site-specific weed management (ESSWM), as the discrimination of small plants (crop and weeds) at early growth stages is subject to serious limitations using other types of remote platforms with coarse spatial resolutions, such as satellite or conventional aerial platforms. Small changes in flight altitude are crucial for low-altitude image acquisition because these variations can cause important differences in the spatial resolution of the ortho-images. Furthermore, a decrease of flying altitude reduces the area covered by each single overlapped image, which implies an increase of both the sequence of images and the complexity of the image mosaicking procedure to obtain an ortho-image covering the whole study area. This study was carried out in two wheat fields naturally infested by broad-leaved and grass weeds at a very early phenological stage. The geometric accuracy differences and crop line alignment among ortho-mosaics created from UAV image series were investigated while taking into account three different flight altitudes (30, 60 and 100 m) and a number of GCPs (from 11 to 45). The results did not show relevant differences in geo-referencing accuracy on the interval of altitudes studied. Similarly, the increase of the number of GCPs did not imply a relevant increase of geo-referencing accuracy. Therefore, the most important parameter to consider when choosing the flying altitude is the ortho-image spatial resolution required rather than the geo-referencing accuracy. Regarding the crop mis-alignment, the results showed that the overall errors were less than twice the spatial resolution, which did not break the crop line continuity at the studied spatial resolutions (pixels from 7.4 to 24.7 mm for 30, 60 and 100 m flying altitudes respectively) on the studied crop (early wheat). The results lead to the conclusion that a UAV flying at a range of 30 to 100 m altitude and using a moderate number of GCPs is able to generate ultra-high spatial resolution ortho-imagesortho-images with the geo-referencing accuracy required to map small weeds in wheat at a very early phenological stage. This is an ambitious agronomic objective that is being studied in a wide research program whose global aim is to create broad-leaved and grass weed maps in wheat crops for an effective ESSWM.     

Assessing GNSS correction signals for assisted guidance systems in agricultural vehicles

Accuracy levels achieved with differential global positioning system (DGPS) receivers in agricultural operations depend upon the quality of the correction signal. This study has assessed differential signal error from a Dedicated Base Station, OmniSTAR VBS, European Geostationary Navigation Overlay System, European reference frame-IP for internet protocol (EUREF-IP) and radio navigation satellite aided technique (RASANT). These signals were utilized in guidance assisting systems for agricultural applications, such as tillage, harvesting, planting and spraying, in which GPS receivers were used under dynamic conditions. Simulations of agricultural operations on different days and at different time slots and simultaneously recording the tractor′s geo-position from a DGPS receiver and the tractor′s geo-position from a real-time kinematic (RTK) GPS allowed the comparison of the GPS correction signals. The hardware used for tractor guidance was a lightbar (Trimble model EZ-Guide Plus) system. ANOVA statistics showed a significant difference between the accuracy of the correction signals from different sources. GPS correction signal recommendations to farmers depend upon the accuracy required for the specific operation: (a) Yield monitoring and soil sampling (<1 m) are possible with all the GPS correction signals accessed in any time slot. (b) Broadcast seeding, fertilizer and herbicide application (<0.5 m) are possible for 80% of time with OmniSTAR VBS, 40% of time with RASANT and EUREF-IP and 100% of time with a dedicated base station. (c) Transplanting and drill seeding (<0.04 m) are not possible with the accuracy correction provided by any one of the systems used in this study.     

Implement lateral position accuracy under RTK-GPS tractor guidance

The accuracy of global positioning system receivers can be improved by differential correction systems (DGPS), which deliver sub-meter accuracy. Higher-accuracy, of about 1 cm on-the-go, is delivered by units generally referred to as real-time kinematic (RTK) DGPS systems. RTK-DGPS systems are presently used by commercial companies for automatic guidance of tractors in row-crop operations. Since high-end DGPS systems with high-accuracy are generally very expensive, it is necessary that the commercial benefit be maximized and that any related errors will be minimized. In the present study, the deviations from a predetermined route of a three-point hitch implement mounted on a RTK-DGPS based automatically guided tractor were recorded and were used to validate that the implement deviation is strongly dependent on the distance from the tractor rear axle. The recorded deviations were analyzed for paved and rough surfaces; the latter caused substantially greater deviations. Based on the above hypothesis, a possible way to improve the performance by reducing the deviations at a point on the implement is suggested.     

基于Delaunay三角网的GNSS控制网闭合环自动搜索算法研究及应用

计算GNSS控制网的同步环和异步环的闭合差是衡量GNSS控制网测量精度的重要依据,然而某些商业GNSS软件不能完全搜索所有闭合环.在建立Delaunay三角网基础上,提出了一种新的GNSS控制网闭合环自动搜索算法,并根据此算法采用C#语言编写软件.结果表明,该算法实现了GNSS控制网所有闭合环的闭合差快速计算和检核,提高了GNSS控制网测量精度,丰富了农业生产实践作业效率.