首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到20条相似文献，搜索用时 31 毫秒
1.
The objective of this study was to compare performance of partial least square regression (PLSR) and best narrowband normalize nitrogen vegetation index (NNVI) linear regression models for predicting N concentration and best narrowband normalize different vegetation index (NDVI) for end of season biomass yield in bioenergy crop production systems. Canopy hyperspectral data was collected using an ASD FieldSpec FR spectroradiometer (350–2500 nm) at monthly intervals in 2012 and 2013. The cropping systems evaluated in the study were perennial grass {mixed grass [50 % switchgrass (Panicum virgatum L.), 25 % Indian grass “Cheyenne” (Sorghastrum nutans (L.) Nash) and 25 % big bluestem “Kaw” (Andropogon gerardii Vitman)] and switchgrass “Alamo”} and high biomass sorghum “Blade 5200” (Sorghum bicolor (L.) Moench) grown under variable N applications rates to estimate biomass yield and quality. The NNVI was computed with the wavebands pair of 400 and 510 nm for the high biomass sorghum and 1500 and 2260 nm for the perennial grass that were strongly correlated to N concentration for both years. Wavebands used in computing best narrowband NDVI were highly variable, but the wavebands from the red edge region (710–740 nm) provided the best correlation. Narrowband NDVI was weakly correlated with final biomass yield of perennial grass (r2 = 0.30 and RMSE = 1.6 Mg ha?1 in 2012 and r2 = 0.37 and RMSE = 4.0 Mg ha?1, but was strongly correlated for the high biomass sorghum in 2013 (r2 = 0.72 and RMSE = 4.6 Mg ha?1). Compared to the best narrowband VI, the RMSE of the PLSR model was 19–41 % lower for estimating N concentration and 4.2–100 % lower for final biomass. These results indicates that PLSR might be best for predicting the final biomass yield using spectral sample obtained in June to July, but narrowband NNVI was more robust and useful in predicting N concentration.  相似文献   

2.
Inexpensive, accurate, and rapid measurements of sodicity are required to identify the restoration options for degraded sites. This study determined the spatial variability of the percent of ammonium acetate extractable Na (%Na), apparent electrical conductivity (ECa), pH1:1, elevation and topographic wetness index, and used this information to create %Na management zones. In an 8.1 ha North Dakota field that contained Natraquolls and Calciaquolls, 1088 soil samples from the 0–0.3 and 0.3–0.6 m were collected from a 12.2 by 12.2 m geo-referenced grid. At each grid point, the elevation and ECa was determined using a differential corrected global positioning system and EM38m, respectively. Soil samples were analyzed for the %Na, EC1:1, pH1:1, and soil dispersion. Exponential semi-variogram models explained 96.7% of the ln-transformed %Na data in the 0–0.3 m soil depth, and %Na was correlated to EC1:1 (r = 0.54), pH1:1 (r = 0.68), clay dispersion (r = 0.68), ECav (r = 0.49), and ECah (r = 0.57). Forward stepwise regression models based on elevation, EC1:1, pH1:1, and ECah explained 64 and 74% of the %Na variability in the surface 0.3 m and subsurface 0.3–0.6 m, respectively. Management zones were identified that reduced the %Na variability up to 82%.  相似文献   

3.
Biomass monitoring is one of the main pillars of precision farm management as it involves deeper knowledge about pest and weed status, soil quality, water stress, and yield prediction, among others. This research focuses on estimating crop biomass from high-resolution red, green, blue imaging obtained with an unmanned aerial vehicle. Onion, as one of the most cultivated vegetables, was studied for two seasons under non-controlled conditions in two commercial plots. Green canopy cover, crop height, and canopy volume (Vcanopy) were the predictor variables extracted from the geomatic products. Strong relationships were found between Vcanopy and dry leaf biomass and dry bulb biomass. Adjusted coefficient of determination (\({\text{R}}_{\text{adj}}^2\)) values were 0.76 and 0.95, respectively. Nevertheless, crop management practices and leaf depletion at vegetative stages significantly affect the accuracy of the canopy model. These results suggested that obtaining biomass using aerial images are a good alternative to other sensors and platforms as they have high spatial and temporal resolution to perform high-quality biomass monitoring.  相似文献   

4.
Protein content, which represents rice taste quality, must be estimated in order to create a harvesting plan as well as next year’s basal dressing fertilizer application plan. Ground-based hyperspectral imaging with high resolution (1 × 1 mm per pixel) was used for estimating the protein content of brown rice before harvest. This paper compares the estimation accuracy of rice protein content estimation models generated from the mean reflectances of five regions of interest (ROIs): the overall target area, dark area (less illuminated parts of the rice plants), canopy area (leaves, yellow leaves, and ears), leaf area, and ear and yellow leaf area. The size of the target sampling area was 0.85 × 0.85 m. An R + G + B histogram and a GNDVI–NDVI image were used to separate the target area into the individual ROIs. The values of the coefficient of determination R 2 and the root mean square error of prediction (RMSE) were similar for each model: R 2 ranged from 0.83 to 0.86 and RMSE ranged from 0.27 to 0.30% for all models except for the dark area model, where R 2 = 0.76 and RMSE = 0.35%. There were no significant differences in the magnitude of the estimation error among all models. This result indicates that it is not necessary to obtain an image with a ground resolution that is greater than 0.85 × 0.85 m per pixel to estimate rice protein content before harvest. This result should provide useful information when deciding the altitude of platforms for imaging rice fields.  相似文献   

5.
Till date, the remote sensing research on crop nutrient monitoring has focused mainly on biomass and nitrogen (N) estimation and only a few attempts had been made to characterize and monitor macronutrients other than N. Field experiments were undertaken to study the remote detection of macronutrient status of rice using hyperspectral remote sensing. The variability in soil available N, phosphorus (P) and sulphur (S) and their content in plants were created using artificial fertility gradient design. The leaf and canopy hyperspectral reflectance was captured from variable macronutrient status vegetation. Linear correlation analysis between the spectral reflectance and plant nutrient status revealed significantly (p < 0.05) higher correlation coefficient at 670, 700, 730, 1090, 1260, 1460 nm for the nutrient under study. Published and proposed vegetation indices (VIs) were tested for canopy N, P and S prediction. The results of the investigation revealed that, published VIs (NDVI hyper and NDVI broadbands) could retrieve canopy N with higher accuracy, but not P and S. The predictability of the visible and short wave infrared based VI NRI1510 ((R1510 ? R660)/(R1510 + R660)) was the highest (r = 0.81, p < 0.01) for predicting N. Based on the outcomes of linear correlation analysis new VIs were proposed for remote detection of P and S. Proposed VI P_670_1260 ((R1260 ? R670)/(R1260 + R670)) retrieved canopy P status with higher prediction accuracy (r = 0.67, p < 0.01), whereas significantly higher canopy S prediction (r = 0.58, p < 0.01) was obtained using VI S_670_1090 ((R1090 ? R670)/(R1090 + R670)). The proposed spectral algorithms could be used for real time and site-specific N, P and S management in rice. Nutrient specific wavelengths, identified in the present investigation, could be used for developing relatively low-cost sensors of hand-held instruments to monitor N, P and S status of rice plant.  相似文献   

6.
  相似文献   

7.
In this paper, a new method to fuse low resolution multispectral and high resolution RGB images is introduced, in order to detect Gramineae weed in rice fields with plants at 50 days after emergence (DAE).The images are taken from a fixed-wing unmanned aerial vehicle (UAV) at 60 and 70 m altitude. The proposed method combines the texture information given by a high resolution red–green–blue (RGB) image and the reflectance information given by a low resolution multispectral (MS) image, to obtain a fused RGB-MS image with better weed discrimination features. After analyzing the normalized difference vegetation index (NDVI) and normalized green red difference index (NGRDI) for weed detection, it was found that NGRDI presents better features. The fusion method consists of decomposing the RGB image using the intensity, hue and saturation (IHS) transformation, then, a second order Haar wavelet transformation is applied to the intensity layer (I) and the NGRDI image. From this transformation, the low–low (LL) coefficients of the NGRDI image are replaced by the LL coefficients of the I layer. Finally, the fused image is obtained by transforming the new wavelet coefficients to RGB space. To test the method, a one hectare experimental plot with rice plants at 50 DAE with Gramineae weeds was selected. Additionally, to compare the performance of the method, two indices were used, specifically, the M/MGT index which is the percentage of detected weed area, and the MP index which indicates the precision of weed detection. These indices were evaluated in four validation zones using three Neural Networks (NN) detection systems based on three types of images; namely, RGB, RGB + NGRDI, and fused RGB-NGRDI. The best weed detection performance was obtained by the NN with the fused image, with M/MGT index between 80 and 108% and MP between 70 and 85%.  相似文献   

8.
This study proposes a new method for inverting radiative transfer models to retrieve canopy biophysical parameters using remote sensing imagery. The inversion procedure is improved with respect to standard inversion, and achieves simultaneous inversion of leaf area index (LAI), soil reflectance (ρsoil), chlorophyll content (Ca+b) and average leaf angle (ALA). In this approach, LAI is used to constrain modelling conditions during the inversion process, providing information about the phenological state of each plot under study. Due to the small area of the vegetation plots used for the inversion procedure and in order to avoid redundant information and improve computation efficiency, existing plot segmentation was used. All retrieved biophysical parameters, except LAI, were assumed to be invariant within each plot. The proposed methodology, based on the combination of PROSPECT and SAILH models, was tested over 16 cereal fields and 51 plots, on two dates, which were chosen to ensure crop assessment at different phenological stages. Plots were selected to provide a wide range of LAI between 0 and 6. Field measurements of LAI, ALA and Ca+b were conducted and used as ground truth for validation of the proposed model-inversion methodology. The approach was applied to very high spatial resolution remote sensing data from the QuickBird 2 satellite. The inversion procedure was successfully applied to the imagery and retrieved LAI with R 2 = 0.83 and RMSE = 0.63 when compared to LAI2000 ground measurements. Separate inversions for barley and wheat yielded R 2 = 0.89 (RMSE = 0.64) and R 2 = 0.56 (RMSE = 0.61), respectively.  相似文献   

9.
This study assessed the capability of several xanthophyll, chlorophyll and structure-sensitive spectral indices to detect water stress in a commercial farm consisting of five fruit tree crop species with contrasting phenology and canopy architecture. Plots irrigated and non-irrigated for eight days of each species were used to promote a range of plant water status. Multi-spectral and thermal images were acquired from an unmanned aerial system while concomitant measurements of stomatal conductance (gs), stem water potential (Ψs) and photosynthesis were taken. The Normalized Difference Vegetation Index (NDVI), red-edge ratio (R700/R670), Transformed Chlorophyll Absorption in Reflectance Index normalized by the Optimized Soil Adjusted Vegetation Index (TCARI/OSAVI), the Photochemical Reflectance Index using reflectance at 530 (PRI) and 515 nm [PRI(570–515)] and the normalized PRI (PRInorm) were obtained from the narrow-band multi-spectral images and the relationship with the in-field measurements explored. Results showed that within the Prunus species, Ψs yielded the best correlations with PRI and PRI(570–515) (r2 = 0.53) in almond trees, with TCARI/OSAVI (r2 = 0.88) in apricot trees and with PRInorm, R700/R670 and NDVI (r2 from 0.72 to 0.88) in peach trees. Weak or no correlations were found for the Citrus species due to the low level of water stress reached by the trees. Results from the sensitivity analysis pointed out the canopy temperature (Tc) and PRI(570–515) as the first and second most sensitive indicators to the imposed water conditions in all the crops with the exception of apricot trees, in which Ψs was the most sensitive indicator at midday. PRInorm was the least sensitive index among all the water stress indicators studied. When all the crops were analyzed together, PRI(570–515) and NDVI were the indices that better correlations yielded with Crop Water Stress Index, gs and, particularly, Ψs (r2 = 0.61 and 0.65, respectively). This work demonstrated the feasibility of using narrow-band multispectral-derived indices to retrieve water status for a variety of crop species with contrasting phenology and canopy architecture.  相似文献   

10.
The Syntermes genus, the most significant termite pest in eucalyptus cultivation, damages roots and debarks plant rings. This can kill the seedlings of this plant, and thus require replanting. Integrated management, based on sampling plans can reduce damage to eucalyptus seedlings and allows the rational use of chemical control. The objectives were to model the spatial distribution of the Syntermes spp. foraging holes using the Matérn-cluster point process in the Cerrado region (Brazilian savannah), simulate a sampling plan for termite hole density, produce maps of foraging hole densities using geostatistics and validate the simulated sampling plan in the field. The distribution of the Syntermes spp. foraging holes was spatially non-homogeneous and it adjusted to the Matérn-cluster point process model in the Minas Gerais Cerrado areas. The best Syntermes sampling plan simulation in the area of Cerrado is to launch a circular 5 m radius parcel every 100 m (sampling error <5 %) in a systematic manner. The approach of point processes, combined with geostatistics, is adequate to produce maps for the termite Syntermes spp. infestation in the eucalyptus plantation.  相似文献   

11.
Assessment of crop health status in real time could provide reliable and useful information for making effective and efficient management decisions regarding the appropriate time and method to control crop diseases and insect damage. In this study, hyperspectral reflectance of symptomatic and asymptomatic rice leaves infected by Pyricularia grisea Sacc, Bipolaris oryzae Shoem, Aphelenchoides besseyi Christie and Cnaphalocrocis medinalis Guen was measured in a laboratory within the 350–2?500 nm spectral region. Principal component analysis was performed to obtain the principal component spectra (PCs) of different transformations of the original spectra, including original (R), common logarithm of reciprocal (lg (1/R)), and the first derivative of original and common logarithm of reciprocal spectra (R′ and (lg (1/R))′). A probabilistic neural network classifier was applied to discriminate the symptomatic rice leaves from asymptomatic ones with the front PCs. For identifying symptomatic and asymptomatic rice leaves, the mean overall discrimination accuracies for R, lg (1/R), R′ and (lg (1/R))′ were 91.3, 93.1, 92.3 and 92%, and the mean Kappa coefficients were 0.771, 0.835, 0.829 and 0.82, respectively. To discriminate between disease and insect damage, the overall accuracies for R, lg (1/R), R′ and (lg (1/R))′ were 97.7, 98.1, 100 and 100%, and the Kappa coefficients were 0.962, 0.97, 1 and 1, respectively. These results demonstrated that hyperspectral remote sensing can discriminate between multiple diseases and the insect damage of rice leaves under laboratory conditions.  相似文献   

12.
Site-specific crop management is a promising approach to maximize crop yield with optimal use of rapidly depleting natural resources. Availability of high resolution crop data at critical growth stages is a key for real-time data-driven decisions during the production season. The goal of this study was to evaluate the possibility of using small unmanned aerial system (UAS)-based remote sensing technologies to monitor the crop stress of irrigated pinto beans (Phaseolus vulgaris L.) with varied irrigation and tillage treatments. A small UAS with onboard multispectral and infrared thermal imaging sensors was used to collect data from bean field plots on three growth stages in 2015 and 2016, respectively. Indicators including green normalized vegetation index (GNDVI), canopy cover (CC, ratio of ground covered by crop canopy to the total plot area) and canopy temperature (CT, °C) of crops were extracted from imaging data and correlated with ground-reference crop yield and leaf area index (LAI) estimated with a handheld ceptometer. Results show that GNDVI, CC and CT were able to differentiate crops with full and deficit irrigation treatments at each of the three growth stages in both years. Developed indicators were strongly correlated with to the crop yield with Pearson correlation coefficients (r) of approximate 0.71 and 0.72 for GNDVI and CC, respectively, in the early growth stage (54 days after planting) in both years. Canopy temperature showed even stronger correlation (r > 0.8) with yield at early growth stage. Performance of small UAS-based imagery-based indicators in crop stress monitoring and crop yield estimation was better than or comparable to that of the ground-based LAI estimates, indicating the potential of such remote sensing tool in rapid crop stress monitoring and management.  相似文献   

13.
LiDAR sensors are widely used in many areas and, in recent years, that includes agricultural tasks. In this work, a self-developed mobile terrestrial laser scanner based on a 2D light detection and ranging (LiDAR) sensor was used to scan an intensive olive orchard, and different algorithms were developed to estimate canopy volume. Canopy volume estimations derived from LiDAR sensor readings were compared to conventional estimations used in fruticulture/horticulture research and the results prove that they are equivalent with coefficients of correlation ranging from r = 0.56 to r = 0.82 depending on the algorithms used. Additionally, tools related to analysis of point cloud data from the LiDAR-based system are proposed to extract further geometrical and structural information from tree row crop canopies to be offered to farmers and technical advisors as digital raster maps. Having high spatial resolution information on canopy geometry (i.e., height, width and volume) and on canopy structure (i.e., light penetrability, leafiness and porosity) may result in better orchard management decisions. Easily obtainable, reliable information on canopy geometry and structure may favour the development of decision support systems either for irrigation, fertilization or canopy management, as well as for variable rate application of agricultural inputs in the framework of precision fruticulture/horticulture.  相似文献   

14.
  相似文献   

15.
Easy-to-capture and robust plant status indicators are important factors when implementing precision agriculture techniques on fields. In this study, aerial red, green and blue color space (RGB) photography and near-infrared (NIR) photography was performed on an experimental field site with nine different cover crops. A lightweight unmanned aerial system (UAS) served as platform, consumer cameras as sensors. Photos were photogrammetrically processed to orthophotos and digital surface models (DSMs). In a first validation step, the spatial precision of RGB orthophotos (x and y, ± 0.1 m) and DSMs (z, ± 0.1 m) was determined. Then, canopy cover (CC), plant height (PH), normalized differenced vegetation index (NDVI), red edge inflection point (REIP), and green red vegetation index (GRVI) were extracted. In a second validation step, the PHs derived from the DSMs were compared with ground truth ruler measurements. A strong linear relationship was observed (R 2 = 0.80?0.84). Finally, destructive biomass samples were taken and compared with the remotely-sensed characteristics. Biomass correlated best with plant height (PH), and good approximations with linear regressions were found (R 2 = 0.74 for four selected species, R 2 = 0.58 for all nine species). CC and the vegetation indices (VIs) showed less significant and less strong overall correlations, but performed well for certain species. It is therefore evident that the use of DSM-based PHs provides a feasible approach to a species-independent non-destructive biomass determination, where the performance of VIs is more species-dependent.  相似文献   

16.
Wheat aphid, Sitobion avenae F. is one of the most destructive insects infesting winter wheat and appears almost annually in northwest China. Past studies have demonstrated the potential of remote sensing for detecting crop diseases and insect damage. This study aimed to investigate the spectroscopic estimation of leaf aphid density by applying continuous wavelet analysis to the reflectance spectra (350–2 500 nm) of 60 winter wheat leaf samples. Continuous wavelet transform (CWT) was performed on each of the reflectance spectra to generate a wavelet power scalogram compiled as a function of wavelength location and scale of decomposition. Linear regression between the wavelet power and aphid density was to identify wavelet features (coefficients) that might be the most sensitive to aphid density. The results identified five wavelet features between 350 and 2 500 nm that provided strong correlations with leaf aphid density. Spectral indices commonly used to monitor crop stresses were also employed to estimate aphid density. Multivariate linear regression models based on six sensitivity spectral indices or five wavelet features were established to estimate aphid density. The results showed that the model with five wavelet features (R2 = 0.72, RMSE = 16.87) performed better than the model with six sensitivity spectral indices (R2 = 0.56, RMSE = 21.19), suggesting that the spectral features extracted through CWT might potentially reflect aphid density. The results also provided a new method for estimating aphid density using remote sensing.  相似文献   

17.
Seven microsatellite loci were evaluated to compare the allele pool of Primorsky honeybee population (n = 90), which was introduced into the Novosibirsk region (south-western Siberia), with the populations of Middle Russian (n = 191, A.m. mellifera), Mountain Grey Caucasian (n =113, A.m. caucasica), Carniolan (n = 61, A.m. carnica) and Carpathian (n = 184, A.m. carpatica) races. The degree of genetic differentiation in Novosibirsk population using a variety of criteria (Fst, Rst (AMOVA), Nei genetic distances) was evaluated.  相似文献   

18.
This paper presents history and ways of creating triploid apple cultivars differing from common diploid ones by less pronounced fruiting periodicity by years, higher marketability and weight of fruits, and self-fertility. In the search for an effective method of mass production of triploid seedlings, crosses of the type 4x × 4x, 4x × 3x, 4x × 2x, 3x × 4x, 3x × 3x, 3x × 2x, 2x × 4x, and 2x × 3x were studied. The most effective way of mass production of triploid seedlings was the 2x × 4x cross (diploid × tetraploid). In the group of 2x × 4x crosses, more than 9000 seedlings from different families were subjected to cytological analysis. The ratio of hybrid progeny in terms of ploidy levels was as follows: 30.3% diploids, 69.5% triploids, 0.2% tetraploids, and 0.04% aneuploids. It was established that triploids can also be obtained in 4x × 2x crosses; however, it is necessary to castrate the flowers in the maternal parent, since all apple tetraploids have high self-fertility. For the first time in the world, a series of triploid cultivars (approximately 20) was obtained from intervalent crosses of the diploid × tetraploid type in the All-Russia Research Institute of Fruit Crop Selection. Particularly interesting are triploid cultivars that have immunity to scab, which include nine cultivars, of which Aleksandr Boiko, Vavilovskoe, Maslovskoe, and Yablochnyi Spas are included in the State Register of breeding achievements allowed for use.  相似文献   

19.
The agronomic characteristics of different legume cover crops and their effects on soil chemical properties were investigated in a short-term field study. We compared weed biomass, nitrogen equivalence, growth rate, leaf chlorophyll content, cover crop biomass, soil total organic carbon, and soil total nitrogen under eight different legume cover crops in a short-term field experiment. We found the highest growth rate, cover crop dry biomass, N plant content, and N contribution with C. ochroleuca plants, whereas for leaf chlorophyll content, soil organic carbon, and total nitrogen the highest values were found with M. pruriens. We did not find any significant difference among C. ochroleuca and M. pruriens for cover crop dry biomass. Our findings suggest that the incorporation of cover crops into the soil can change positively the soil chemical properties, such as soil organic carbon and total nitrogen. Our results also highlight the importance of considering the short-term effect of cover crops on the tropical soil fertility maintenance, in this case, Regosol.  相似文献   

20.
This is a first-time assessment of the direct and indirect effects of hydrothermal conditions on pathogenesis of root infections in cultivated varieties of spring wheat and barley. Long-term field research (2000–2015) was carried out in the area of risk farming combined with laboratory experiments. The effects of the phytosanitary condition of soil, seeds, and underground plant organs were taken into account. It was found that soil pathogenic population and the development of soil-borne infections largely depend on hydrothermal factors. The development of root rot of spring wheat was stimulated by arid conditions during tillering and heading: the disease rate was increased by 33.5% compared to the optimal moisture supply against a background of a high plant pathogen population of the soil. In drought conditions, the number of saprotrophic microorganisms decreased 3.3 times and suppressive soil activity fell 3.0 times provoking root infections. Microorganisms consuming inorganic forms of nitrogen and cellulolytic agents were found to be highly sensitive to hydrothermal factors. Arid conditions increased the plants’ susceptibility to the inoculum of soil origin, since the increase in the number of conidia in the inoculum from 5–15 to 150–180 per 1 g of soil increased the frequency of infections by root rots by 7.8 times, especially on the epicotyl and the base of the stem. Damage of root rot was increased by pest flies Oscinella frit L., O. pusilla Mg., Phorbia genitalis Schnb., and Mayetiola destructor Say. Their activity increased in warm, arid conditions. Drought-resistant gramineous weeds Panicum miliaceum ssp. ruderales L. (Kitag.) Tzvei., Setaria glauca (L.) Beauv., Avena fatua L., Setaria viridis (L.) Beauv. competed with the crop and consequently increased the development of root rot by 20% or more in dry years. Seeds of gramineous weeds, multiplied after dry years, contributed to reproduction and survival of many soil-borne phytopathogens. Grain ripening in moistened conditions led to transmission of the root rot agents Bipolaris sorokiniana Sacc. Shoem. (syn. Helminthosporium sativum Pam., King et Bakke) and Fusarium fungi via seeds. This led to proliferation of root rot in the germination phase and significantly (53%) affected the cereals’ germ.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号