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1.
Spectral unmixing techniques can be used to quantify crop canopy cover within each pixel of an image and have the potential
for mapping the variation in crop yield. This study applied linear spectral unmixing to airborne hyperspectral imagery to
estimate the variation in grain sorghum yield. Airborne hyperspectral imagery and yield monitor data recorded from two sorghum
fields were used for this study. Both unconstrained and constrained linear spectral unmixing models were applied to the hyperspectral
imagery with sorghum plants and bare soil as two endmembers. A pair of plant and soil spectra derived from each image and
another pair of ground-measured plant and soil spectra were used as endmember spectra to generate unconstrained and constrained
soil and plant cover fractions. Yield was positively related to the plant fraction and negatively related to the soil fraction.
The effects of variation in endmember spectra on estimates of cover fractions and their correlations with yield were also
examined. The unconstrained plant fraction had essentially the same correlations ( r) with yield among all pairs of endmember spectra examined, whereas the unconstrained soil fraction and constrained plant
and soil fractions had r-values that were sensitive to the spectra used. For comparison, all 5151 possible narrow-band normalized difference vegetation
indices (NDVIs) were calculated from the 102-band images and related to yield. Results showed that the best plant and soil
fractions provided better correlations than 96.3 and 99.9% of all the NDVIs for fields 1 and 2, respectively. Since the unconstrained
plant fraction could represent yield variation better than most narrow-band NDVIs, it can be used as a relative yield map
especially when yield data are not available. These results indicate that spectral unmixing applied to hyperspectral imagery
can be a useful tool for mapping the variation in crop yield. 相似文献
2.
The objective of this study was to evaluate the crop monitoring performance of a consumer-grade camera with non-modified and modified spectral ranges which are commonly used in low-altitude unmanned aerial vehicle (UAV) platforms. The camera was fixed sequentially with seven types of filters for collecting visible images and near-infrared (NIR) images with different center band locations and bandwidths. Meanwhile, field-based hyperspectral data and normalized difference vegetation index (NDVI) measured by a GreenSeeker handheld crop sensor (GS-NDVI) were collected to examine the accuracy of rapeseed growth monitoring in terms of vegetation indices (VIs) derived from UAV images. Results showed that the UAV-based RGB-VIs and optimal NIR-VIs had similar accuracy for predicting GS-NDVI. Moreover, similar results were achieved based on the hyperspectral data, indicating the importance of spectral characteristics for GS-NDVI estimation. However, the UAV-based results also indicated that the performance of VIs derived from the band combinations containing longer NIR center wavelengths and narrower bandwidths was obviously poorer than that of the RGB-VIs. The image quality of the NIR band was also found to be inferior to the visible band based on quantitative analysis, which also revealed that image quality had great impact on UAV-based results. Image quality was then related to the effects of camera exposure, spectral sensitivity, soil background and dark areas. The results from this study provide useful information for camera modifications by selecting appropriate filters that not only are sensitive to crop growth, but also ensure image quality. 相似文献
3.
Many hyperspectral vegetation indices (VIs) have been developed to estimate crop nitrogen (N) status at leaf and canopy levels.
However, most of these indices have not been evaluated for estimating plant N concentration (PNC) of winter wheat ( Triticum aestivum L.) at different growth stages using a common on-farm dataset. The objective of this study was to evaluate published VIs
for estimating PNC of winter wheat in the North China Plain for different growth stages and years using data from both N experiments
and farmers’ fields, and to identify alternative promising hyperspectral VIs through a thorough evaluation of all possible
two band combinations in the range of 350–1075 nm. Three field experiments involving different winter wheat cultivars and
4–6 N rates were conducted with cooperative farmers from 2005 to 2007 in Shandong Province, China. Data from 69 farmers’ fields
were also collected to evaluate further the published and newly identified hyperspectral VIs. The results indicated that best
performing published and newly identified VIs could explain 51% (R 700/R 670) and 57% (R 418/R 405), respectively, of the variation in PNC at later growth stages (Feekes 8–10), but only 22% (modified chlorophyll absorption
ratio index, MCARI) and 43% (R 763/R 761), respectively, at the early stages (Feekes 4–7). Red edge and near infrared (NIR) bands were more effective for PNC estimation
at Feekes 4–7, but visible bands, especially ultraviolet, violet and blue bands, were more sensitive at Feekes 8–10. Across
site-years, cultivars and growth stages, the combination of R 370 and R 400 as either simple ratio or a normalized difference index performed most consistently in both experimental ( R
2 = 0.58) and farmers’ fields ( R
2 = 0.51). We conclude that growth stage has a significant influence on the performance of different vegetation indices and
the selection of sensitive wavelengths for PNC estimation, and new approaches need to be developed for monitoring N status
at early growth stages. 相似文献
4.
A cropping system is usually characterized by continuous spatio-temporal vegetation variability. Vegetation variability can be detected by changes in several vegetation parameters defined according to purpose. Estimation of these vegetation parameters has been made possible by calculating various vegetation indices (VIs), usually by ratioing, differencing, ratioing differences and sums, or by forming linear combinations of spectral band data. Spectrometers or sensors have been used to acquire visible and infrared spectral properties of vegetation. This paper presents a ground-based hyperspectral imaging system for characterizing vegetation spectral features. The hyperspectral sensor used was a ground-based line sensor, ImSpector (V10-12-102), with a nominal spectral resolution of 1.5–2 nm and a wavelength range of 360–1010 nm. A graphical user interface (GUI) was developed in a MATLAB environment to aid in processing and analysis of acquired multidimensional spectral image data. Issues that arise when applying the imaging system to a particular field include acquiring hyperspectral images, selecting appropriate vegetation features or VIs, and quantifying the selected vegetation features or indices with the GUI developed. Vegetation features extracted by the proposed imaging system contribute not only to monitoring vegetation variability in crop systems, but also provide a potential source of relevant variables that can be used to estimate various vegetation parameters. A study that was set up to investigate the alternate bearing phenomenon of citrus trees illustrates the basic elements of the proposed approach. 相似文献
6.
Sensor-based methods of analysis to assess dry matter yield and quality constituents of crops are time- and labour-saving,
and can facilitate site-specific management. Nevertheless, standard nadir measurements of maize ( Zea mays cv. Ambrosius), based on top-of-canopy reflectance, are difficult due to plant heights of more than three metres. This study
was conducted to explore the potential of off-nadir field spectral measurements for the non-destructive prediction of dry
matter yield (DM), metabolisable energy (ME) and crude protein (CP) in total biomass in a maize canopy. Plants were measured
at five different heights (0–50, 50–100, 100–50, 150–200 and 200–250 cm above the soil) at three zenith view angles (60°,
75° and 90°, respectively). Modified partial least squares regression was used for analysis of the hyperspectral data (355–2300 nm
and 620–1000 nm). Optimum combinations of angle and height as well as an optimum one-sensor-strategy were determined for DM
yield, CP and ME in total biomass. Coefficients of determination for off-nadir measurements were compared to nadir measurements;
the results showed improved prediction accuracies for DM yield and ME using off-nadir measurements, but not for CP for which
nadir measurements were better. 相似文献
7.
The aim of this study was to evaluate the accuracy of the spectro-optical, photochemical reflectance index (PRI) for quantifying the disease index (DI) of yellow rust ( Biotroph Puccinia striiformis) in wheat ( Triticum aestivum L.), and its applicability in the detection of the disease using hyperspectral imagery. Over two successive seasons, canopy reflectance spectra and disease index (DI) were measured five times during the growth of wheat plants (3 varieties) infected with varying amounts of yellow rust. Airborne hyperspectral images of the field site were also acquired in the second season. The PRI exhibited a significant, negative, linear, relationship with DI in the first season ( r 2 = 0.91, n = 64), which was insensitive to both variety and stage of crop development from Zadoks stage 3–9. Application of the PRI regression equation to measured spectral data in the second season yielded a coefficient of determination of r 2 = 0.97 ( n = 80). Application of the same PRI regression equation to airborne hyperspectral imagery in the second season also yielded a coefficient of determination of DI of r 2 = 0.91 ( n = 120). The results show clearly the potential of PRI for quantifying yellow rust levels in winter wheat, and as the basis for developing a proximal, or airborne/spaceborne imaging sensor of yellow rust in fields of winter wheat. 相似文献
8.
Variable-rate technologies and site-specific crop nutrient management require real-time spatial information about the potential for response to in-season crop management interventions. Thermal and spectral properties of canopies can provide relevant information for non-destructive measurement of crop water and nitrogen stresses. In previous studies, foliage temperature was successfully estimated from canopy-scale (mixed foliage and soil) temperatures and the multispectral Canopy Chlorophyll Content Index (CCCI) was effective in measuring canopy-scale N status in rainfed wheat ( Triticum aestivum L.) systems in Horsham, Victoria, Australia. In the present study, results showed that under irrigated wheat systems in Maricopa, Arizona, USA, the theoretical derivation of foliage temperature unmixing produced relationships similar to those in Horsham. Derivation of the CCCI led to an r
2 relationship with chlorophyll a of 0.53 after Zadoks stage 43. This was later than the relationship ( r
2 = 0.68) developed for Horsham after Zadoks stage 33 but early enough to be used for potential mid-season N fertilizer recommendations. Additionally, ground-based hyperspectral data estimated plant N (g kg −1) in Horsham with an r
2 = 0.86 but was confounded by water supply and N interactions. By combining canopy thermal and spectral properties, varying water and N status can potentially be identified eventually permitting targeted N applications to those parts of a field where N can be used most efficiently by the crop. 相似文献
9.
Increased availability of hyperspectral imagery necessitates the evaluation of its potential for precision agriculture applications. This study examined airborne hyperspectral imagery for mapping cotton ( Gossypium hirsutum L.) yield variability as compared with yield monitor data. Hyperspectral images were acquired using an airborne imaging system from two cotton fields during the 2001 growing season, and yield data were collected from the fields using a cotton yield monitor. The raw hyperspectral images contained 128 bands between 457 and 922 nm. The raw images were geometrically corrected, georeferenced and resampled to 1 m resolution, and then converted to reflectance. Aggregation functions were then applied to each of the 128 bands to reduce the cell resolution to 4 m (close to the cotton picker's cutting width) and 8 m. The yield data were also aggregated to the two grids. Correlation analysis showed that cotton yield was significantly related to the image data for all the bands except for a few bands in the transitional range from the red to the near-infrared region. Stepwise regression performed on the yield and hyperspectral data identified significant bands and band combinations for estimating yield variability for the two fields. Narrow band normalized difference vegetation indices derived from the significant bands provided better yield estimation than most of the individual bands. The stepwise regression models based on the significant narrow bands explained 61% and 69% of the variability in yield for the two fields, respectively. To demonstrate if narrow bands may be better for yield estimation than broad bands, the hyperspectral bands were aggregated into Landsat-7 ETM+ sensor's bandwidths. The stepwise regression models based on the four broad bands explained only 42% and 58% of the yield variability for the two fields, respectively. These results indicate that hyperspectral imagery may be a useful data source for mapping crop yield variability. 相似文献
10.
High resolution satellite imagery has the potential to map within-field variation in crop growth and yield. This study examined
SPOT 5 satellite multispectral imagery for estimating grain sorghum yield. A 60 km × 60 km SPOT 5 scene and yield monitor
data from three grain sorghum fields were recorded in south Texas. The satellite scene contained four spectral bands (green,
red, near-infrared and mid-infrared) with a 10-m spatial resolution. Subsets were extracted from the scene that covered the
three fields. Images with pixel sizes of 20 and 30 m were also generated from the individual field images to simulate coarser
resolution satellite imagery. Vegetation indices and principal components were derived from the images at the three spatial
resolutions. Grain yield was related to the vegetation indices, the four bands and the principal components for each field,
and for all the fields combined. The effect of the mid-infrared band on estimates of yield was examined by comparing the regression
results from all four bands with those from the other three bands. Statistical analysis showed that the 10-m, four-band image
and the aggregated 20-m and 30-m images explained 68, 76 and 83%, respectively, of the variation in yield for all the fields
combined. The coefficient of determination between yield and the imagery increased with pixel size because of the smoothing
effect. The inclusion of the mid-infrared band slightly improved the R
2 values. These results indicate that high resolution SPOT 5 multispectral imagery can be a useful data source for determining
within-field yield variation for crop management. 相似文献
11.
Early and accurate diagnosis is a critical first step in mitigating losses caused by plant diseases. An incorrect diagnosis can lead to improper management decisions, such as selection of the wrong chemical application that could potentially result in further reduced crop health and yield. In tomato, initial disease symptoms may be similar even if caused by different pathogens, for example early lesions of target spot (TS) caused by the fungus Corynespora cassicola and bacterial spot (BS) caused by Xanthomonas perforans. In this study, hyperspectral imaging (380–1020 nm) was utilized in laboratory and field (collected by an unmanned aerial vehicle; UAV) settings to detect both diseases. Tomato leaves were classified into four categories: healthy, asymptomatic, early and late disease development stages. Thirty-five spectral vegetation indices (VIs) were calculated to select an optimum set of indices for disease detection and identification. Two classification methods were utilized: (i) multilayer perceptron neural network (MLP), and (ii) stepwise discriminant analysis (STDA). Best wavebands selection was considered in blue (408–420 nm), red (630–650 nm) and red edge (730–750 nm). The most significant VIs that could distinguish between healthy leaves and diseased leaves were the photochemical reflectance index (PRI) for both diseases, the normalized difference vegetation index (NDVI850) for BS in all stages, and the triangular vegetation index (TVI), NDVI850 and chlorophyll index green (Chl green) for TS asymptomatic, TS early and TS late disease stage respectively. The MLP classification method had an accuracy of 99%, for both BS and TS, under field (UAV-based) and laboratory conditions. 相似文献
12.
Quick and low cost delineation of site-specific management zones (SSMZ) would improve applications of precision agriculture. In this study, a new method for delineating SSMZ using object-oriented segmentation of airborne imagery was demonstrated. Three remote sensing domains—spectral, spatial, and temporal- are exploited to improve the SSMZ relationship to yield. Common vegetation indices (VI), and first and second derivatives ( \(\rho^{\prime}\), \(\rho^{\prime\prime}\)) from twelve airborne hyperspectral images of a cotton field for one season \(\rho^{\prime}\) were used as input layers for object-oriented segmentation. The optimal combination of VI, SSMZ size and crop phenological stage were used as input variables for SSMZ delineation, determined by maximizing the correlation to segmented yield monitor maps. Combining narrow band vegetation indices and object-oriented segmentation provided higher correlation between VI and yield at SSMZ scale than that at pixel scale by reducing multi-resource data noise. VI performance varied during the cotton growing season, providing better SSMZ delineation at the beginning and middle of the season (days after planting (DAP) 66–143).The optimal scale determined for SSMZ delineation was approximately 240 polygons for the study field, but the method also provided flexibility enabling the setting of practical scales for a given field. For a defined scale, the optimal single phenological stage for the study field was near July 11 (DAP 87) early in the growing season. SSMZs determined from multispectral VIs at a single stage were also satisfactory; compared to hyperspectral indices, temporal resolution of multi-spectral data seems more important for SSMZ delineation. 相似文献
13.
In most cases, statistical models for monitoring the disease severity of yellow rust are based on hyperspectral information.
The high cost and limited cover of airborne hyperspectral data make it impossible to apply it to large scale monitoring. Furthermore,
the established models of disease detection cannot be used for most satellite images either because of the wide range of wavelengths
in multispectral images. To resolve this dilemma, this paper presents a novel approach by constructing a spectral knowledge
base (SKB) of diseased winter wheat plants, which takes the airborne images as a medium and links the disease severity with
band reflectance from environment and disaster reduction small satellite images (HJ-CCD) accordingly. Through a matching process
with a SKB, we estimated the disease severity with a disease index (DI) and degrees of disease severity. The proposed approach
was validated against both simulated data and field surveyed data. Estimates of DI (%) from simulated data were more accurate,
with a coefficient of determination ( R
2) of 0.9 and normalized root mean square error (NRMSE) of 0.2. The overall accuracy of classification reached 0.8, with a
kappa coefficient of 0.7. Validation of the estimates against field measurements showed that there were some errors in the DI value
with the NRMSE close to 0.5. The result of the classification was more encouraging with an overall accuracy of 0.77 and a
kappa coefficient of 0.58. For the matching process, Mahalanobis distance performed better than the spectral angle (SA) in all
analyses in this study. The potential of SKB for monitoring the incidence and severity of yellow rust is illustrated in this
study. 相似文献
14.
In viticulture, it is critical to predict productivity levels of the different vineyard zones to undertake appropriate cropping practices. To overcome this challenge, the final yield was predicted by combining vegetation indices (VIs) to sense the health status of the crop and by computer vision to obtain the vegetated fraction cover (Fc) as a measure of plant vigour. Multispectral imagery obtained from an unmanned aerial vehicle (UAV) is used to obtain VIs and Fc, which are used together with artificial neural networks (ANN) to model the relationship between VIs, Fc and yield. The proposed methodology was applied in a vineyard, where different irrigation and fertilisation doses were applied. The results showed that using computer vision techniques to differentiate between canopy and soil is necessary in precision viticulture to obtain accurate results. In addition, the combination of VIs (reflectance approach) and Fc (geometric approach) to predict vineyard yield results in higher accuracy (root mean square error (RMSE)?=?0.9 kg vine?1 and relative error (RE)?=?21.8% for the image when close to harvest) compared to the simple use of VIs (RMSE?=?1.2 kg vine?1 and RE?=?28.7%). The implementation of machine learning techniques resulted in much more accurate results than linear models (RMSE?=?0.5 kg vine?1 and RE?=?12.1%). More precise yield predictions were obtained when images were taken close to the harvest date, although promising results were obtained at earlier stages. Given the perennial nature of grapevines and the multiple environmental and endogenous factors determining yield, seasonal calibration for yield prediction is required. 相似文献
15.
Spectral mixture analysis and hyperspectral remote sensing are analytical and hardware tools new to precision agriculture. They can allow detection and identification of various crop stresses and other plant and canopy characteristics through analysis of their spectral signatures. One stressor in cotton, the strawberry spider mite ( Tetranychus
turkestani U.N.), feeds on plants causing leaf puckering and reddish discoloration in early stages of infestation and leaf drop later. To determine the feasibility of detecting the damage caused by this pest at the field level, AVIRIS imagery was collected from USDA-ARS cotton research fields at Shafter, CA on 4 dates in 1999. Additionally, cotton plants and soil were imaged in situ in 10 nm increments from 450 to 1050 nm with a liquid-crystal tunable-filter camera system. Mite-damaged areas on leaves, healthy leaves, tilled shaded soil, and tilled sunlit soil were chosen as reference endmembers and used in a constrained linear spectral mixture analysis to unmix the AVIRIS data producing fractional abundance maps. The procedure successfully distinguished between adjacent mite-free and mite-infested cotton fields although shading due to sun angle differences between dates was a complicating factor. The resulting healthy plant, soil, mite-damaged, and shade fraction maps showed the distribution and relative abundance of these endmembers in the fields. These hardware and software technologies can identify the location, spatial extent, and severity of crop stresses for use in precision agriculture. 相似文献
16.
Productivity and botanical composition of legume-grass swards in rotation systems are important factors for successful arable
farming in both organic and conventional farming systems. As these attributes vary considerably within a field, a non-destructive
method of detection while doing other tasks would facilitate more targeted management of crops and nutrients in the soil–plant–animal
system. Two pot experiments were conducted to examine the potential of field spectroscopy to assess total biomass and the
proportions of legume, using binary mixtures and pure swards of grass and legumes. The spectral reflectance of swards was
measured under artificial light conditions at a sward age ranging from 21 to 70 days. Total biomass was determined by modified
partial least squares (MPLS) regression, stepwise multiple linear regression (SMLR) and the vegetation indices (VIs) simple
ratio (SR), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and red edge position (REP). Modified
partial least squares and SMLR gave the largest R
2 values ranging from 0.85 to 0.99. Total biomass prediction by VIs resulted in R
2 values of 0.87–0.90 for swards with large leaf to stem ratios; the greatest accuracy was for EVI. For more mature and open
swards VI-based detection of biomass was not possible. The contribution of legumes to the sward could be determined at a constant
biomass level by the VIs, but this was not possible when the level of biomass varied. 相似文献
17.
Soybean crop plays an important role in world food production and food security, and agricultural production should be increased accordingly to meet the global food demand. Satellite remote sensing data is considered a promising proxy for monitoring and predicting yield. This research aimed to evaluate strategies for monitoring within-field soybean yield using Sentinel-2 visible, near-infrared and shortwave infrared (Vis/NIR/SWIR) spectral bands and partial least squares regression (PLSR) and support vector regression (SVR) methods. Soybean yield maps (over 500 ha) were recorded by a combine harvester with a yield monitor in 15 fields (3 farms) in Paraná State, southern Brazil. Sentinel-2 images (spectral bands and 8 vegetation indices) across a cropping season were correlated to soybean yield. Information pooled across the cropping season presented better results compared to single images, with best performance of Vis/NIR/SWIR spectral bands under PLSR and SVR. At the grain filling stage, field-, farm- and global-based models were evaluated and presented similar trends compared to leaf-based hyperspectral reflectance collected at the Brazilian National Soybean Research Center. SVR outperformed PLSR, with a strong correlation between observed and predicted yield. For within-field soybean yield mapping, field-based SVR models (developed individually for each field) presented the highest accuracies. The results obtained demonstrate the possibility of developing within-field yield prediction models using Sentinel-2 Vis/NIR/SWIR bands through machine learning methods. 相似文献
18.
This paper investigated the possibility of discriminating tomato yellow leaf curl disease by a hyperspectral imaging technique. A hyperspecral imaging system collected hyperspectral images of both healthy and infected tomato leaves. The reflectance spectra, first derivative reflectance spectra and absolute reflectance difference spectra in the wavelength range of 500–1000 nm of both background and the leaf area were analyzed to select sensitive wavelengths and band ratios. 853 nm was selected to create a mask image for background segmentation, while 720 nm from the reflectance spectra, four peaks (560, 575, 712, and 729 nm) from the first derivative spectra and, four wavelengths with higher values (586, 720 nm) and lower values (690, 840 nm) in the absolute difference spectra were selected as a set of sensitive wavelengths. Four band ratio images (560/575, 712/729, 586/690, and 720/840 nm) were compared with four widely used vegetation indices (VIs). 24 texture features were extracted using grey level co-occurrence matrix (GLCM), respectively. The performance of each feature was evaluated by receiver operator characteristic (ROC) curve analysis. The best threshold values of each feature were calculated by Yonden’s index. Mean value of correlation (COR_MEAN) extracted from the band ratio image (720/840 nm) had the best performance, whose AUC value was 1.0. The discrimination result for a validation set based on its best threshold value was 100%. This research also demonstrated that multispectral images at 560, 575 and 720 nm have a potential for detecting tomato yellow leaf curl virus infection in field applications. 相似文献
19.
A field experiment of 18 wheat cultivars of erectophile, planophile and horizontal canopy architectures was conducted during
the 2004–2005 growing seasons in Beijing (40°10.6′ N, 116°26.3′ E), China. Canopy reflectance (350–2500 nm) at different growth
stages was measured and leaf area index (LAI) and leaf chlorophyll concentration (Chl) were determined at booting. The main
objective of the study was to evaluate the ability of various vegetative indices (VIs) to detect canopy architectures in wheat
genotypes. The chlorophyll-sensitive spectral indices, the modified chlorophyll absorption reflectance index (MCARI) and the
transformed chlorophyll absorption reflectance index (TCARI), were very sensitive to canopy architectures in the wheat plants.
The MCARI values were significantly ( p < 0.05) larger for the horizontal genotypes than for the planophile ones, and also larger for the planophile genotypes than
for the erectophile ones for the six growth stages. The TCARI had a similar power to MCARI for discriminating between different
wheat canopy architectures. At booting, both MCARI and TCARI were only weakly related to Chl in the upper, middle and lower
leaves. The results emphasized the difficulties of determining crop Chl from canopy reflectance. The mechanisms that cause
the differences in MCARI and TCARI among the canopy architectures are discussed. 相似文献
20.
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. 相似文献
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