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1.
The accuracy of a single sensor is often low because all proximal soil sensors respond to more than one soil property of interest. Sensor data fusion can potentially overcome this inability of a single sensor and can best extract useful and complementary information from multiple sensors or sources. In this study, a data fusion was performed of a Vis?CNIR spectrometer and an EM38 sensor for multiple soil properties. Stepwise multiple linear regression (SMLR), partial least squares regression (PLSR) and principal components analysis combined with stepwise multiple linear regression (PCA?+?SMLR) methods were used in three different fields. Soil properties investigated for data fusion included soil texture (clay, silt and sand), EC, pH, total organic carbon (TOC), total nitrogen (TN) and carbon to nitrogen ratio (CN). It was found that soil property models based on fusion methods significantly improved the accuracy of predictions of soil properties measureable by both sensors, such as clay, silt, sand, EC and pH from those based on either of the individual sensors. The accuracy of predictions of TOC, TN and CN was also improved in some cases, but was not consistent in all fields. Among data fusion methods, PLSR outperformed both SMLR and PCA?+?SMLR methods because it proved to have a better ability to deal with the multi-collinearity among the predictor variables of both sensors. The best data fusion results were found in a clayey field and the worst in a sandy field. It is concluded that sensor data fusion can enhance the quality of soil sensing in precision agriculture once a proper set of sensors has been selected for fusion to estimate desired soil properties. More efficient statistical data analysis methods are needed to handle a large volume of data effectively from multiple sensors for sensor data fusion. 相似文献
2.
Brian Krienke Richard B. Ferguson Michael Schlemmer Kyle Holland David Marx Kent Eskridge 《Precision Agriculture》2017,18(6):900-915
Ground-based active sensors have been used in the past with success in detecting nitrogen (N) variability within maize production systems. The use of unmanned aerial vehicles (UAVs) presents an opportunity to evaluate N variability with unique advantages compared to ground-based systems. The objectives of this study were to: determine if a UAV was a suitable platform for use with an active crop canopy sensor to monitor in-season N status of maize, if UAV’s were a suitable platform, is the UAV and active sensor platform a suitable substitute for current handheld methods, and is there a height effect that may be confounding measurements of N status over crop canopies? In a 2013 study comparing aerial and ground-based sensor platforms, there was no difference in the ability of aerial and ground-based active sensors to detect N rate effects on a maize crop canopy. In a 2014 study, an active sensor mounted on a UAV was able to detect differences in crop canopy N status similarly to a handheld active sensor. The UAV/active sensor system (AerialActive) platform used in this study detected N rate differences in crop canopy N status within a range of 0.5–1.5 m above a relatively uniform turfgrass canopy. The height effect for an active sensor above a crop canopy is sensor- and crop-specific, which needs to be taken into account when implementing such a system. Unmanned aerial vehicles equipped with active crop canopy sensors provide potential for automated data collection to quantify crop stress in addition to passive sensors currently in use. 相似文献
3.
Within-field variability of plant-available nutrients often results in different fertilizer requirements across a field. There is uncertainty concerning the efficacy of alternative sampling strategies suitable for site-specific management. This study compared various soil sampling approaches for P, K, pH, and organic matter (OM) in eight agricultural fields. Soil samples were collected using an intensive 0.2-ha grid-point procedure, and were used to compare less intensive sampling approaches. The approaches were based on 1.2–1.6-ha grid cells (Grid), soil series of digitized soil survey maps (SSM), soil series of detailed soil survey (1:12,000 scale) maps, elevation zones, and management zones based on various information layers (ZS). The approaches varied in reducing the within-unit soil-test variability and maximizing mean soil-test values across sampling units, but none was superior across all fields and nutrients. All approaches were less efficient for P and K than for pH or OM. The Grid and ZS approach were the most effective across all nutrients and fields. However, the Grid approach was more effective for P, the Grid and ZS approaches were better for K and pH, and the SSM and ZS approaches were better for OM. The ZS approach often resulted in fewer sampling zones than the Grid approach, which implies lower soil testing costs for producers, but required more knowledge and subjective judgement than a Grid approach to adapt it to field-specific conditions. 相似文献
4.
Dong Rui Miao Yuxin Wang Xinbing Yuan Fei Kusnierek Krzysztof 《Precision Agriculture》2022,23(3):939-960
Rapid methods allowing for non-destructive crop monitoring are imperative for accurate in-season nitrogen (N) status assessment and precision N management. The objectives of this paper were to (1) compare the performance of a leaf fluorescence sensor Dualex 4 and an active canopy reflectance sensor Crop Circle ACS-430 for estimating maize (Zea mays L.) N status indicators across growth stages; (2) evaluate the potential of N status prediction across growth stages using the reflectance parameters acquired from the canopy sensor at an early growth stage; and, (3) investigate the prospect of combining the active canopy sensor and leaf fluorescence sensor data to estimate N nutrition index (NNI) indirectly using a general model across growth stages. The results indicated that data from both sensors were closely related to NNI across stages. However, using the direct NNI estimation method, among the tested indices, only the N balance index (NBI) could diagnose N status satisfactorily, based on the Kappa statistics. The effect of growth stages on proximal sensing was reduced by incorporating the information of days after sowing. It was found that the leaf fluorescence sensor performed relatively better in estimating plant N concentration whereas the canopy reflectance sensor performed better in aboveground biomass estimation. Their combination significantly improved the reliability of N diagnosis, including NNI prediction. In addition, the study confirmed that N status can be assessed by predicting aboveground biomass at the later stages using the canopy reflectance measurements at an early stage. Furthermore, the integrated NBI was verified to be a more robust and sensitive N status indicator than the chlorophyll concentration index. It is concluded that combining active canopy sensor data, of an early growth stage (e.g. V8), with leaf fluorescence sensor data, modified using days after sowing, can improve the accuracy of corn N status diagnosis across growth stages.
相似文献5.
Bare soil reflectance from airborne imagery or laboratory spectrometers has been used to infer soil properties such as soil texture, organic matter, water content, salinity and crop residue cover. However, the relation of soil properties to reflectance data often varies with soil type and conditions and surface reflectance may not be representative of the conditions in the root zone. The objectives of this study were to assess the soil reflectance data obtained by ground-based sensors and to model soil properties in the root zone as a function of surface soil reflectance and plant response. Ground-based sensors were used to simultaneously monitor soil and canopy reflectance in the visible and near-infrared (VNIR) along six rows and in two growth stages in a 7 ha cotton field. The reflectance data were compared to soil properties, leaf nutrients and biomass measured at 33 sampling positions along the rows. Brightness values of the blue and green bands of soil reflectance were better correlated to soil water content, particulate organic matter and extractable potassium and phosphorus, while those in the red and NIR bands were correlated to soil carbonate content, total nitrogen, electrical conductivity and foliar nutrients. The correlation of red soil reflectance with canopy reflectance was significant and indicated an indirect inverse relationship between soil fertility and plant stress. The integration of surface soil reflectance and plant response variables in a multiple regression model did not substantially improve the prediction of soil properties in the root zone. However, crop nutrient status explained a significant portion of the spatial variability of soil properties related to nitrification processes when soil reflectance did not. The implication of these findings to agricultural management is discussed. 相似文献
6.
Normalization of uncalibrated late-season digital aerial imagery for evaluating corn nitrogen status
Using uncalibrated digital aerial imagery (DAI) for diagnosing in-season nitrogen (N) status of corn (Zea mays L.) is challenging because of the dynamic nature of corn growth and the difficulty of obtaining timely imagery. Late-season
DAI is more accurate for identifying areas deficient in N than early-season imagery. Even so, the quantitative use of the
imagery across many fields is still limited because DAI is often not radiometrically calibrated. This study tested whether
spectral characteristics of corn canopy derived from normalized uncalibrated late-season DAI could predict final corn N status.
Color and near-infrared (NIR) imagery was collected in late August or early September across Iowa from 683 corn fields in
2006, 824 in 2007, and 828 fields in 2007. Four sampling areas (one within a target-deficient area) were selected within each
field for conducting the end-of-season corn stalk nitrate test (CSNT). Each image was enhanced to increase the dynamic range
within each field and to normalize reflectance values across all fields within a year. The reflectance values of individual
bands and three vegetation indices were used to predict corn N status expressed as Deficient and Sufficient (a combination
of marginal, optimal, and excessive CSNT categories) using a binary logistic regression (BLR). The green reflectance had the
highest prediction rate, which was 70, 64, and 60% in 2006, 2007, and 2008, respectively. The results suggest that the normalized
(enhanced) late-season uncalibrated DAI can be used to predict final corn N status in large-scale on-farm evaluation studies. 相似文献
7.
[目的]研究县域土壤全氮含量的空间分布和采样数量,为紫色土丘陵区采样提供参考。[方法]利用协同克里格法,以初始的1 777个土壤全氮含量数据为随机抽取的数据,分别随机抽取1 599、1 421和1 243个数据为目标变量,并以初始的1 777个土壤有机质数据为辅助变量,对四川省罗江县土壤全氮含量进行插值分析,从而利用协同克里格法对县域尺度下农田土壤全氮含量在不同样点数量下空间分布中的适用性进行评价。[结果]在相同取样数量下,全氮协同克里格法的均方根误差相对于普通克里格法降低0.019 6%～0.072 5%,预测值和实测值之间的相关系数提高0.69%～0.90%。利用协同克里格法,土壤全氮含量数据在缩减30%情况下,其估值精度高于1 777个样点下的普通克里格估值,且二者的分布图都具有较高的拟合度。[结论]协同克里格法是一种经济、精准的方法,可为县域土壤养分含量的空间分布提供基础信息。 相似文献
8.
基于高光谱的土壤有机质含量预测模型的建立与评价 总被引：17，自引：1，他引：17
【目的】土壤有机质含量是反映土壤肥力的重要特征，利用高光谱技术对有机质（OM）含量进行定量化反演为土壤信息化管理和资源评价提供了重要的依据。【方法】利用ASD2500高光谱仪在室内条件下测定了风干土壤样品的可见—近红外光谱，分析了不同区域范围土壤光谱反射率曲线形状变化和土壤有机质含量的变化特点，并针对东北地区以黑土为主的土样光谱反射率不同变换形式与有机质含量进行了相关性分析。【结果】结果表明，有机质含量较高的黑土的光谱曲线与其它土壤类型的光谱曲线在形状上有很大差异，即在600～900 nm附近，以黑龙江土样为代表的东北黑土表现为直缓上升，而河南和山东的潮土则表现为曲陡上升。相关分析结果表明，土壤有机质含量与原始光谱反射率在545～830 nm呈显著负相关，其中在580～738 nm波段范围内达到极显著负相关。与一阶导数光谱相关性进一步增强，在481～598 nm呈现极显著负相关，而在816～932 nm和1 039～1 415 nm波段范围内具有极显著的正相关性。土壤有机质含量与部分波段处的吸收深度和反射峰高度也表现为不同程度的相关性。【结论】利用570～590 nm波段的一阶导数光谱和1 280 nm处反射峰高度P_Depth1280可以较好地预测东北主要土壤类型有机质含量。在此基础上建立了土壤有机质含量的高光谱反演模型并进行了验证。 相似文献
9.
Is data needed from every field to determine in-season precision nitrogen recommendations in winter wheat? 总被引：1，自引：0，他引：1
The research reported here seeks to determine whether it is necessary to obtain optical reflectance measurements with a GreenSeeker® handheld sensor from each field to make accurate in-season nitrogen application recommendations for winter wheat, and how much precision—and profit—would be lost by moving from site-specific (or field-specific) optical reflectance sampling to region-level sampling. The approach used was to estimate a separate linear response-plateau regression every year using yield and optical reflectance data from randomized complete block experiments. Profits from region-level sampling and field-level sampling were statistically indistinguishable, but this result was mostly due to both being imprecise. Furthermore, the region- and field-based sampling systems were no better than break-even with the historical extension advice to apply preplant anhydrous ammonia at 90 kg ha?1. The approach of estimating a new regression every year is too imprecise, whether at the field or region level. This research goes beyond past research by accounting for the uncertainty in the estimated relationships. The poor performance of the systems is directly related to the imprecise relationship between yield and optical reflectance responses to nitrogen. 相似文献
10.
土壤含水率对近红外传感器标定模型的响应研究 总被引：1，自引：0，他引：1
为提高近红外传感器测量的准确度,进一步理解不同标定模型对土壤含水率测量精度的影响。利用土壤表面的近红外反射光强来预测土壤含水率,通过归一化处理将反射光强转化为相对吸收深度和相对反射率,采用2种标定方法,分别建立土壤含水率与相对吸收深度之间及土壤含水率与相对反射率之间的线性模型与非线性模型。选取我国东北地区的黑土进行标定,并用独立的试验数据对模型进行检验。结果表明,吸收深度法的线性和非线性模型的预测值和实测值符合度较好。反射率法的线性模型和非线性模型对土壤的含水率预测均方根误差(RMSE)分别为2.89%和2.95%,相对吸收深度法非线性模型的RMSE值明显大于其他3种模型,预测准确度最低。说明不同标定方法会影响土壤含水率的预测结果。4种模型的预测精度能够满足测量要求。 相似文献
11.
J. Berntsen A. Thomsen K. Schelde O. M. Hansen L. Knudsen N. Broge H. Hougaard R. Hørfarter 《Precision Agriculture》2006,7(2):65-83
Several methods were developed for the redistribution of nitrogen (N) fertilizer within fields with winter wheat (Triticum aestivum L.) based on plant and soil sensors, and topographical information. The methods were based on data from nine field experiments
in nine different fields for a 3-year period. Each field was divided into 80 or more subplots fertilized with 60, 120, 180
or 240 kg N ha−1. The relationships between plot yield, N application rate, sensor measurements and the interaction between N application
and sensor measurements were investigated. Based on the established relations, several sensor-based methods for within-field
redistribution of N were developed. It was shown that plant sensors predicted yield at harvest better than soil sensors and
topographical indices. The methods based on plant sensors showed that N fertilizer should be moved from areas with low and
high sensor measurements to areas with medium values.
The theoretical increase in yield and N uptake, and the reduced variation in grain protein content resulting from the application
of the above methods were estimated. However, the estimated increases in crop yield, N-uptake and reduced variation in grain
protein content were small. 相似文献
12.
In recent years, real-time technology has been introduced into the practice of spraying variable fungicide rates in cereal
fields. Plant parameters for characterising heterogeneous plant growth such as biomass or plant surface area can be indirectly
detected by the sensor CROP-Meter. The sensor signal is correlated with the Leaf Area Index, which can be used to adapt the
application rate. However, this relatively simple method of controlling variable-rate fungicide application does not take
into account the differences in disease distribution. In practice, decision support systems such as proPlant expert.classic
can provide information about disease infection probabilities, application time, fungicide products and application rates
for uniform spraying. A prototype of the system proPlant expert.precise was developed to estimate infection risks from fungal
diseases using weather and field-specific data for up to three management areas with different yield expectations. The system
also considers economic factors such as expected yield and costs of the fungicide products in generating a spraying map with
different fungicide dosages. The information from the CROP-Meter (sensor) and from the decision support system proPlant expert.precise
(map) was combined to provide a real-time spraying system with map overlay. The system was tested in 2007 in three winter
wheat fields. Compared with conventional uniform spraying the CROP-Meter with map overlay treatment resulted in up to 32.6%
fungicide savings (CROP-Meter versus uniform: up to 20.3%). There was no yield reduction on average when the sensor-controlled
spraying technologies were used. 相似文献
13.
Improved Prediction and Reduction of Sampling Density for Soil Salinity by Different Geostatistical Methods 总被引：3，自引：0，他引：3
LI Yan SHI Zhou WU Ci-fang LI Hong-yi LI Feng 《中国农业科学(英文版)》2007,6(7):832-841
The spatial estimation for soil properties was improved and sampling intensities also decreased in terms of incorporated auxiliary data. In this study, kriging and two interpolation methods were proven well to estimate auxiliary variables： cokriging and regression-kriging, and using the salinity data from the first two stages as auxiliary variables, the methods both improved the interpolation of soil salinity in coastal saline land. The prediction accuracy of the three methods was observed under different sampling density of the target variable by comparison with another group of 80 validation sample points, from which the root-mean-square error （RMSE） and correlation coefficient （r） between the predicted and measured values were calculated. The results showed, with the help of auxiliary data, whatever the sample size of the target variable may be, cokriging and regression-kriging performed better than ordinary kriging. Moreover, regression-kriging produced on average more accurate predictions than cokriging. Compared with the kriging results, cokriging improved the estimations by reducing RMSE from 23.3 to 29% and increasing r from 16.6 to 25.5%, regression-kriging improved the estimations by reducing RMSE from 25 to 41.5% and increasing r from 16.8 to 27.2%. Therefore, regression-kriging shows promise for improved prediction for soil salinity and reduction of soil sampling intensity considerably while maintaining high prediction accuracy. Moreover, in regression-kriging, the regression model can have any form, such as generalized linear models, non-linear models or tree-based models, which provide a possibility to include more ancillary variables. 相似文献
14.
15.
Breure T. S. Haefele S. M. Hannam J. A. Corstanje R. Webster R. Moreno-Rojas S. Milne A. E. 《Precision Agriculture》2022,23(4):1333-1353
Modern sensor technologies can provide detailed information about soil variation which allows for more precise application of fertiliser to minimise environmental harm imposed by agriculture. However, growers should lose neither income nor yield from associated uncertainties of predicted nutrient concentrations and thus one must acknowledge and account for uncertainties. A framework is presented that accounts for the uncertainty and determines the cost–benefit of data on available phosphorus (P) and potassium (K) in the soil determined from sensors. For four fields, the uncertainty associated with variation in soil P and K predicted from sensors was determined. Using published fertiliser dose–yield response curves for a horticultural crop the effect of estimation errors from sensor data on expected financial losses was quantified. The expected losses from optimal precise application were compared with the losses expected from uniform fertiliser application (equivalent to little or no knowledge on soil variation). The asymmetry of the loss function meant that underestimation of P and K generally led to greater losses than the losses from overestimation. This study shows that substantial financial gains can be obtained from sensor-based precise application of P and K fertiliser, with savings of up to £121 ha?1 for P and up to £81 ha?1 for K, with concurrent environmental benefits due to a reduction of 4–17 kg ha?1 applied P fertiliser when compared with uniform application.
相似文献16.
Combined use of hyperspectral VNIR reflectance spectroscopy and kriging to predict soil variables spatially 总被引：1，自引：0，他引：1
Hyperspectral visible near infrared reflectance spectroscopy (VNIRRS) and geostatistical methods are considered for precision
soil mapping. This study evaluated whether VNIR or geostatistics, or their combined use, could provide efficient approaches
for assessing the soil spatially and associated reductions in sample size using soil samples from a 32 ha area (800 × 400 m)
in northern Turkey. Soil variables considered were CaCO3, organic matter, clay, sand and silt contents, pH, electrical conductivity, cation exchange capacity (CEC) and exchangeable
cations (Ca, Mg, Na and K). Cross-validation was used to compare the two approaches using all grid data (n = 512), systematic selections of 13, 25 and 50% of the data and random selections of 13 and 25% for calibration; the remaining
data were used for validation. Partial least squares regression (PLSR) analysis was used for calibrating soil properties from
first derivative VNIR reflectance spectra (VNIRRS), whereas ordinary-, co- and regression-kriging were used for spatial prediction.
The VNIRRS-PLSR method provided better prediction results than ordinary kriging for soil organic matter, clay and sand contents,
(R
2 values of 0.56–0.73, 0.79–0.85, 0.65–0.79, respectively) and smaller root mean squared errors of prediction (values of 2.7–4.1,
37.4–43, 46.9–61, respectively). The EC, pH, Na, K and silt content were predicted poorly by both approaches because either
the variables showed little variation or the data were not spatially correlated. Overall, the prediction accuracy of VNIRRS-PLSR
was not affected by sample size as much as it was for ordinary kriging. Cokriging (COK) and regression kriging (RK) were applied
to a combination of values predicted by VNIR reflectance spectroscopy and measured in the laboratory to improve the accuracy
of prediction of the soil properties. The results showed that both COK and RK with VNIRRS estimates improved the predictions
of soil variables compared to VNIRRS and OK. The combined use of VNIRRS and multivariate geostatistics results in better spatial
prediction of soil properties and enables a reduction in sampling and laboratory analyses. 相似文献
17.
Early ventures into site-specific management involved fertilizer management decisions based on soil chemical properties characterized by some form of grid sampling. This is both labor and capital intensive and practitioners quickly began investigating other methods to get a measure of spatial variability. Aerial photographs, which were mainly used to evaluate and assess crop status, allow for the collection of whole-field data at relatively low cost. Our objective is to determine what relationships exist between aerial spectral data and intensive grid soil test results and whether this information can be used to improve future soil sampling strategies. Soil-test organic matter (OM) and Bray-1 P concentrations were measured on soil samples taken using an alternating 12.2- by 24.4-m grid in late March 1994 from a quarter section under center pivot irrigation. Spectral data were collected in the spring of 1996 prior to planting using a multispectral network of digital cameras. Correlations of brightness values from the blue, green, and NIR bands with both OM and Bray-1 P were significant, but relatively low. Normality tests revealed that brightness values for the spectral data sets were generally evenly distributed while those of the soil test OM and Bray-1 P were positively skewed. Many of the very high soil-test data values were due to past management. When those values were removed from the database, greater correlations between spectral data and soil test data were obtained. These results substantiated that aerial imagery can be used to improve sampling strategies, but it must be used in conjunction with existing knowledge and past management histories. 相似文献
18.
D. Franzen D. Long A. Sims J. Lamb F. Casey J. Staricka M. Halvorson V. Hofman 《Precision Agriculture》2011,12(4):594-606
A four-year study was conducted from 2000 to 2004 at eight field sites in Montana, North Dakota and western Minnesota. Five
of these sites were in North Dakota, two were in Montana and one was in Minnesota. The sites were diverse in their cropping
systems. The objectives of the study were to (1) evaluate data from aerial photographs, satellite images, topographic maps,
soil electrical conductivity (ECa) sensors and several years of yield to delineate field zones to represent residual soil nitrate and (2) determine whether
the use of data from several such sources or from a single source is better to delineate nitrogen management zones by a weighted
method of classification. Despite differences in climate and cropping, there were similarities in the effectiveness of delineation
tools for developing meaningful residual soil nitrate zones. Topographic information was usually weighted the most because
it produced zones that were more correlated to actual soil residual nitrate than any other source of data at all locations.
The soil ECa sensor created better correlated zones at Minot, Williston and Oakes than at most eastern sites. Yield data for an individual
year were sometimes useful, but a yield frequency map that combined several years of standardized yield data was more useful.
Satellite imagery was better than aerial photographs at most locations. Topography, satellite imagery, yield frequency maps
and soil ECa are useful data for delineating nutrient management zones across the region. Use of two or more sources of data resulted
in zones with a stronger correlation with soil nitrate. 相似文献
19.
20.
Mats Söderström Thomas Börjesson Carl-Göran Pettersson Knud Nissen Olle Hagner 《Precision Agriculture》2010,11(6):587-599
This paper examines the prediction of within-field differences in protein in malting barley at a late growth stage using the
Yara N-Sensor and prediction of its regional variation with medium resolution satellite images. Field predictions of protein
in the crop at a late growth stage could be useful for harvest planning, whereas regional prediction of barley quality before
harvest would be useful for the grain industry. The project was carried out in central Sweden where the variation in protein
content of malting barley has been documented both within fields and regionally. Scanning with an N-sensor and crop sampling
were carried out in 2007 and 2008 at several fields. The regional data used consisted of weather data, quality analyses of
the malting barley delivered to the major farmers’ co-operative, crops grown and field boundaries. Satellite scenes (SPOT
5 and IRS-P6 LISS-III) were acquired from a date as close as possible to the N-sensor scans. Reasonable partial least squares
(PLS) models could be constructed based on weather and reflectance data from either the N-sensor or satellite. The models
used mainly reflectance data, but the weather data improved them. Better field models could be created with data from the
N-sensor than from the satellite image, but a local satellite-based model based on a simple ratio (middle infrared/green)
in combination with weather was useful in regional prediction of malting barley protein. A regional prediction model based
only on the weather variables explained about half the variation in recorded protein. 相似文献