Wheat Plant Density Research Articles

Retrieval of Fractional Vegetation Cover from Remote Sensing Image of Unmanned Aerial Vehicle Based on Mixed Pixel Decomposition Method

FVC (fractional vegetation cover) is highly correlated with wheat plant density in the reviving period, which is an important indicator for conducting variable-rate nitrogenous topdressing. In this study, with the objective of improving inversion accuracy of wheat plant density, an innovative approach of retrieval of FVC values from remote sensing images of a UAV (unmanned aerial vehicle) was proposed based on the mixed pixel decomposition method. Firstly, remote sensing images of an experimental wheat field were acquired by using a DJI Mini UAV and endmembers in the image were identified. Subsequently, a linear unmixing model was used to subdivide mixed pixels into components of vegetation and soil, and an abundance map of vegetation was acquired. Based on the abundance map of vegetation, FVC was calculated. Consequently, a linear regression model between the ground truth data of wheat plant density and FVC was established. The coefficient of determination (R2), RMSE (root mean square error), and RRMSE (Relative-RMSE) of the inversion model were calculated as 0.97, 1.86 plants/m2, and 0.677%, which indicates strong correlation between the FVC of mixed pixel decomposition method and wheat plant density. Therefore, we can conclude that the mixed pixel decomposition model of the remote sensing image of a UAV significantly improved the inversion accuracy of wheat plant density from FVC values, which provides method support and basic data for variable-rate nitrogenous fertilization in the wheat reviving period in the manner of precision agriculture.

Plant Density Estimation Using UAV Imagery and Deep Learning

Plant density is a significant variable in crop growth. Plant density estimation by combining unmanned aerial vehicles (UAVs) and deep learning algorithms is a well-established procedure. However, flight companies for wheat density estimation are typically executed at early development stages. Further exploration is required to estimate the wheat plant density after the tillering stage, which is crucial to the following growth stages. This study proposed a plant density estimation model, DeNet, for highly accurate wheat plant density estimation after tillering. The validation results presented that (1) the DeNet with global-scale attention is superior in plant density estimation, outperforming the typical deep learning models of SegNet and U-Net; (2) the sigma value at 16 is optimal to generate heatmaps for the plant density estimation model; (3) the normalized inverse distance weighted technique is robust to assembling heatmaps. The model test on field-sampled datasets revealed that the model was feasible to estimate the plant density in the field, wherein a higher density level or lower zenith angle would degrade the model performance. This study demonstrates the potential of deep learning algorithms to capture plant density from high-resolution UAV imageries for wheat plants including tillers.

The influence of crop density and sowing delay on weed germination in winter wheat

Due to changes in agronomic practices and cropping systems such as the development of reduced soil tillage systems, monocropping; changing climate conditions that result in longer warmer periods in autumn and due to other factors such as herbicide resistance approaches for integrated weed management are needed. Crop competitiveness could be one of several measures of cultural weed control, which is an important factor in integrated weed control. Delayed sowing dates or higher crop seed rates could be an integrated tool for weed management, because higher crop plant densities generally are more competitive with weeds. Two field trials were conducted to investigate the influence of winter wheat plant density and delay of sowing date at the Institute of Agriculture, LRCAF in conventional soil tillage system over the period of 2014 –-2016. Winter wheat var. ‘Ada’ was drilled at three different seeding rates (2, 4 and 8 million germinal seeds per hectare) at the regional recommended sowing date, three weeks later and six weeks later. Main weeds in winter wheat crops were annual dicotyledonous, common for the winter cereals stands in Lithuania such as Thlaspi arvense, Viola arvensis, Galium aparine and Lamium purpureum. Furthermore, volunteer oilseed rape and the annual monocotyledonous weed Apera spica-venti were presented in the field. Admittedly, meteorological conditions were favourable for cereals vegetation during the autumn. In plots with the highest seeding rate weed biomass was significantly lower, however lodging problem, especially in early seeded plots occurred. The weed biomass in plots drilled at the end of September was significantly lower compared to early drilled plots. The lowest weed mass was recorded in plots with the latest sowing date, however a significant decrease in grain yield was recorded in these plots.

Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery

Plant density is useful variable that determines the fate of the wheat crop. The most commonly used method for plant density quantification is based on visual counting from ground level. The objective of this study is to develop and evaluate a method for estimating wheat plant density at the emergence stage based on high resolution imagery taken from UAV at very low altitude with application to high throughput phenotyping in field conditions. A Sony ILCE α5100L RGB camera with 24Mpixels and equipped with a 60mm focal length lens was flying aboard an hexacopter at 3 to 7m altitude at about 1m/s speed. This allows getting ground resolution between 0.20mm to 0.45mm, while providing 59–77% overlap between images. The camera was looking with 45° zenith angle in a compass direction perpendicular to the row direction to maximize the cross section viewed of the plants and minimize the effect of the wind created by the rotors. Agisoft photoscan software was then used to derive the position of the cameras for each image. Images were then projected on the ground surface to finally extract subsamples used to estimate the plant density. The extracted images were first classified to separate the green pixels from the background and the rows were then identified and extracted. Finally, image object (group of connected green pixels) was identified on each row and the number of plants they contain was estimated using a Support Vector Machine whose training was optimized using a Particle Swarm Optimization.Three experiments were conducted in Gréoux, Avignon and Clermont sites with some variability in the sowing dates, densities, genotypes, flight altitude, and growth stage at the time of the image acquisition. The application of the method on the 270 samples available over the three sites provides a RMSE and relative RMSE on estimates of 34.05 plants/m2 and 14.31% with a bias of 9.01 plants/m2. However, differences in performances were observed between the three sites, mostly related to the growth stage at the time of the flight. Plants should have between one to two leaves when images are taken. Further, a specific sensitivity analysis shows that the ground resolution of the images should be better than 0.40mm. Finally, the repeatability of the method is good especially when images are taken from similar observational geometries. The current limits and possible improvements of the method proposed are finally discussed.

Estimation of Wheat Plant Density at Early Stages Using High Resolution Imagery.

Crop density is a key agronomical trait used to manage wheat crops and estimate yield. Visual counting of plants in the field is currently the most common method used. However, it is tedious and time consuming. The main objective of this work is to develop a machine vision based method to automate the density survey of wheat at early stages. RGB images taken with a high resolution RGB camera are classified to identify the green pixels corresponding to the plants. Crop rows are extracted and the connected components (objects) are identified. A neural network is then trained to estimate the number of plants in the objects using the object features. The method was evaluated over three experiments showing contrasted conditions with sowing densities ranging from 100 to 600 seeds⋅m-2. Results demonstrate that the density is accurately estimated with an average relative error of 12%. The pipeline developed here provides an efficient and accurate estimate of wheat plant density at early stages.

Winter Wheat Genotype Effect on Canopy Reflectance: Implications for Using NDVI for In‐Season Nitrogen Topdressing Recommendations

Active optical sensors (AOSs) measure crop reflectance at specific wavelengths and calculate vegetation indices (VIs) that are used to prescribe variable N fertilization. Visual observations of winter wheat (Triticum aestivumL.) plant greenness and density suggest that VI values may be genotype specific. Some sensor systems use correction coefficients to eliminate the effect of genotype on VI values. This study was conducted to assess the effects of winter wheat cultivars and growing conditions on canopy reflectance, as measured by red or amber normalized difference vegetative indices (NDVIs) derived from AOSs. Variations in NDVI values among three wheat cultivars were measured at three growth stages (Zadoks 31, 37, and 65) during 3 yr at three sites in Poland. GreenSeeker Model 505 and Crop Circle ACS‐210 sensors were utilized to measure red and amber NDVIs, respectively. Significant (p< 0.05) differences in both forms of NDVI associated with wheat genotypes were observed across years and sites at Zadoks 31, the time when canopy sensing and N fertilization decisions are often made. Lack of a genotype × site interaction for both red and amber NDVIs and the presence of a significant genotype × year interaction for both VIs suggested that (i) canopy greenness and density of the same genotype measured at the same growth stage are likely to be stable across different growing conditions, and (ii) NDVI values for a particular genotype tend to vary more across years than across sites. Because developing temporally variable correction coefficients is not practical, we strongly recommend that an in situ calibration (based on in‐field or a virtual reference strip) is utilized to normalize NDVI across genotypes, years, and sites.

The Morphological Features and Canopy Parameters as Factors Affecting the Competition Between Winter Wheat Varieties and Weeds

Abstract The aim of the study was to determine the relationship between morphological features and canopy parameters of winter wheat varieties and weed infestation. The 2-year-long field experiments were conducted at 3 locations, on 13 varieties of winter wheat cultivated on organic farms. The response of the tested cultivars to weed infestation was similar regardless of the location. The number of weeds did not differ significantly between the wheat varieties and was similar in all locations and years of the study (109-122 plants/m2). The weed dry matter was significantly lower in Chomentowo (38 g/m2) compared to Osiny and Chwałowice (66 and 85 g/ m2). Nateja and Legenda wheat varieties showed the biggest competitiveness against weeds in both years and locations, whereas Alcazar, Boomer and Jenga were characterized as having the least competitive ability. Natula, Kohelia, Batuta and Ostroga wheat varieties showed a different response to weed infestation over the years. The analysis of correlation and cluster analysis showed that wheat dry matter, plant density, and wheat height had the greatest impact on the number and dry matter of weeds. There was no significant correlation between number of tillers per plant and weed infestation parameters measured in the dough stage

Volunteer Barley Interference in Spring Wheat Grown in a Zero-Tillage System

There is no published information on the impact of volunteer barley on wheat yield loss or on the economics of controlling barley with a herbicide. With the registration of imazamox-resistant wheat, it is now possible to control volunteer barley in wheat. Thus, the likelihood of growing wheat in rotation with barley may increase. Field experiments were conducted in 2003 and 2004 at Beaverlodge, Lacombe, and Edmonton, AB, Canada, and Saskatoon, SK, Canada, to determine the impact of volunteer barley on yield of imazamox-resistant spring wheat seeded at relatively low (100 kg ha−1) and high (175 kg ha−1) rates. Barley was seeded at different densities to simulate volunteer barley infestations. Regression analysis indicated that wheat-plant density influenced the effects of volunteer barley interference on wheat yield loss, economic threshold values, and volunteer barley fecundity among locations and years. Economic thresholds varied from as few volunteer barley plants as 3 m−2at Beaverlodge in 2003 and 2004 to 48 m−2at Lacombe in 2003. In most cases, wheat yield loss and volunteer barley fecundity were lower and economic thresholds were higher when wheat was seeded at the higher rate. For example, averaged over both years at Beaverlodge initial slope values (percentage of wheat yield loss at low barley density) were 4.5 and 1.7%, and economic threshold values of volunteer barley plants were 3 m−2and 8 m−2at low and high wheat seeding rates, respectively. Results indicate that volunteer barley can be highly competitive in wheat, but yield losses and wheat seed contamination due to volunteer barley can be alleviated by seeding wheat at a relatively high rate.

First Quantitative Study of Rove Beetles in Oklahoma Winter Wheat Fields

Adult rove beetles (Staphylinidae) were sampled every 7–14 days from one winter wheat field located in each of the four major wheat growing regions of Oklahoma during the 1999–2000 and 2000–2001 growing seasons. The number of cereal aphids per tiller, wheat plant growth stage, and wheat tiller density also were estimated. A total of 12 genera representing 13 species of beetles were collected from the field. The density of rove beetles was generally low, ranging from 0.003 beetles per m2 in fall to 0.106 beetles per m2 in spring. Rove beetle communities differed among seasons. After accounting for the effect of season, there was no statistically significant association between rove beetle community structure and field location, aphid density, wheat plant growth stage, or wheat plant density. Most rove beetle species showed no association with a particular season, however, Aleochara notula Erichson, Lathrobium sp., and Oxypoda sp. were present predominantly in fall, while Bisnius inquitus Erichson was associated with winter. Oxypoda sp. was the most abundant rove beetle in winter wheat fields in spring and was relatively abundant in winter, but was not collected from wheat fields in fall. Tachyporus jocosus Say was present in wheat fields during all seasons. T. jocosus was the most abundant rove beetle species in the winter wheat fields in fall and winter and was the second most abundant species during spring.

Glyphosate-resistant wheat persistence in western Canadian cropping systems

Abstract As a weed, wheat has recently gained greater profile. Determining wheat persistence in cropping systems will facilitate the development of effective volunteer wheat management strategies. In October of 2000, glyphosate-resistant (GR) spring wheat seeds were scattered on plots at eight western Canada sites. From 2001 to 2003, the plots were seeded to a canola–barley–field-pea rotation or a fallow–barley–fallow rotation, with five seeding systems involving seeding dates and soil disturbance levels, and monitored for wheat plant density. Herbicides and tillage (in fallow systems) were used to ensure that no wheat plants produced seed. Seeding systems with greater levels of soil disturbance usually had greater wheat densities. Volunteer wheat densities at 2 (2002) and 3 (2003) yr after seed dispersal were close to zero but still detectable at most locations. At the end of 2003, viable wheat seeds were not detected in the soil seed bank at any location. The majority of wheat seedlings were recruited i...

Spring wheat seed size and seeding rate effects on yield loss due to wild oat (Avena fatua) interference

The development of competitive cropping systems could minimize the negative effects of wild oat competition on cereal grain yield, and in the process, help augment herbicide use. A 3-yr field experiment was conducted at Kalispell, MT, to investigate the effects of spring wheat seed size and seeding rate on wheat spike production, biomass, and grain yield under a range of wild oat densities. Wheat plant density, spikes, biomass, and yield all increased as seed size and seeding rates increased. Averaged across all other factors, the use of higher seeding rates and larger seed sizes improved yields by 12 and 18%, respectively. Accordingly, grain yield was more highly correlated with seed size than with seeding rate effects. However, the combined use of both tactics resulted in a more competitive cropping system, improving grain yields by 30%. Seeding rate effects were related to spike production, whereas seed size effects were related to biomass production. As such, plants derived from large seed appear to have greater vigor and are able to acquire a larger share of plant growth factors relative to plants derived from small seed.

Wheat Plant Density Influences Jointed Goatgrass (Aegilops cylindrica) Competitiveness1

Jointed goatgrass is a problem weed in winter wheat production areas of the Great Plains. Winter wheat seeding rates are easily adjusted by the growers and influence competition by some weeds. Field experiments were initiated in Kansas, Nebraska, and Wyoming using winter wheat cultivars selected from leading adapted cultivars from each region to determine the effect of wheat plant density in the fall on jointed goatgrass competitiveness. Three winter wheat seeding rates (50, 67, and 84 kg seeds/ha) were used at Hays, KS, and Sidney, NE, and four seeding rates (33, 50, 67, and 101 kg seeds/ha) were used at Torrington and Archer, WY. An analysis of covariance model was fit with winter wheat fall plant density as the covariate. In 1996, winter wheat grain contamination (dockage) was reduced at the rate of about 6% for every 10 additional wheat plants/m2 above the threshold density of 70 plants/m2 at Archer, WY, and at the rate of about 0.5% for every 10 additional wheat plants/m2 above the threshold density of 110 plants/m2 at Hays, KS. At Hays the reduction occurred only with the semidwarf cultivar ‘Vista’. Increased wheat density reduced jointed goatgrass reproductive tillers in four out of six location–year combinations and biomass in two out of four location–year combinations. Despite the lack of a consistent reduction in jointed goatgrass competitiveness as the result of increased wheat density, increased seeding rates may be a good, low-cost, long-term investment as part of an integrated jointed goatgrass control program in winter wheat.Nomenclature: Jointed goatgrass, Aegilops cylindrica Host. #3 AEGCY; winter wheat, Triticum aestivum L.Additional index words: Interference; plant competition.

Wheat (Triticum aestivum) Row Spacing, Seeding Rate, and Cultivar Affect Interference from Rye (Secale cereale)1

Two field experiments were conducted in Oklahoma to determine whether changing wheat row spacing, seeding rate, and cultivar would reduce interference from rye. Wheat row spacing did not affect rye seed production. Averaged over row spacing, increasing wheat seeding rate from 67 to 134 kg/ha reduced rye seed production 21 and 25% in two experiments. At one site, grain yield of rye-infested wheat was increased 27 and 23% by doubling wheat seeding rate in 10- and 20-cm rows. Doubling the seeding rate of rye-infested wheat in 30-cm rows did not increase wheat yield. At a second site when data were pooled over row spacings, increasing the wheat seeding rate from 67 to 101 kg/ha increased yield of rye-infested wheat 21%, but yield was still 36% less than for rye-free wheat. In two additional experiments, rye seeds in harvested wheat were reduced 36% by increasing the wheat seeding rate from 60 to 162 kg/ha, whereas yield of rye-infested wheat was increased 82%. Nine cultivars were compared for competitive ability against rye in four experiments. Of 32 possible cultivar location situations, the rye-induced yield loss of ‘Jagger’ wheat was less than 16 other cultivar location situations, whereas the yield loss of ‘Triumph 64’ wheat was less than 10 other cultivar location situations. Mature wheat height, wheat yield, and wheat maturity classification were each negatively correlated to rye yield in infested wheat at two of four sites. Wheat plant density, head density, sunlight interception, and fall cover capability classification were each negatively correlated to rye yield at one of four sites.Nomenclature: Rye, Secale cereale L.; wheat, Triticum aestivum L. ‘2163’, ‘2180’, ‘Agseco 7853’, ‘Jagger’, ‘Karl 92’, ‘Longhorn’, ‘TAM 107’, ‘TAM 202’, ‘Triumph 64’.Additional index words: Winter wheat, competitive ability.Abbreviations: PAR, photosynthetically active radiation.

Prediction of wheat yield loss due to competition by wild oats (Avena spp.)

Field experiments were conducted to determine the effect of varying the density of wheat (cv. Gamut) and wild oats (Avena fatua and A. sterilis ssp. ludoviciana) on the grain yield of wheat. Wheat grain yields were reduced by wild oats through the reduction in number of tillers, and this competitive effect could be reduced by increasing the density of wheat. Increasing wheat plant density beyond the weed-free optimum was found to be unsatisfactory for wild oat control. The best model to describe the data comprised a parabolic wheat yield-density component and a hyperbolic yield loss component. Parameters in the yield-density component were calibrated using 36 data sets to account for variation in sowing date and available soil water. The parabolic model best described the effect of limiting soil water on the wheat yield-density relationship. The yield loss component, which included weed and crop density as variables, indicated that wheat and wild oats behaved as near-equal competitors.The predictive ability of the yield loss model varied when tested on several independent data sets collected from commercial wheat crops. This variation was caused by changes in the relative competitive ability of the crop and weed which resulted in significant variation in values of the yield loss parameters. Further refinement and generalization of the model will be possible if the yield loss parameters can be related to genetic and environmental variables.

Wild Oat (Avena fatua) Competition with Spring Wheat: Plant Density Effects

Field experiments were conducted to measure the grain yield of wheat (Triticum aestivumL. ‘ANZA’) at various wild oat (Avena fatuaL. ♯ AVEFA) and wheat plant densities. Wheat yield declined as wild oat plant density increased. Wheat yield increased in wild oat-infested plots as wheat plant density increased. Regression models were developed to describe the combined effect of wheat and wild oat plant densities on wheat grain yield. Wheat yields were best described by a nonlinear regression model using the relative density of wild oat in the weed-crop stand as the dependent variable. Inclusion of crop stand as a competitive factor significantly improved the fit of all regression models tested. Wild oat were more competitive against wheat in these experiments than in experiments reported by others. Possible reasons for differences in observed competitiveness are discussed.

Potassium uptake by plants, as characterized by root density, species and K/Rb ratio

A small fraction of the plant K requirement is attained by root interception. The bulk of K has to be transported to the growing roots by mass-flow and diffusion in which diffusion mechanism plays the major role. Studies were undertaken to evaluate soil and plant parameters that might have influence on K supply mechanisms in soil and on plant uptake of K. Increasing wheat plant density led to competition for K absorption and resulted in lower K uptake by plant. In high plant density treatment, about 60% of the K requirement was met by diffusion process whereas in low plant density treatment mass-flow contributed most of the K demand. Solution diffusion and mass-flow were the major mechanisms of K supply to wheat roots. The mechanism of K supply to wheat root was compared with corn and onion. The major mechanism of K supply to corn and onion roots was exchange and solution diffusion. The mechanism of K supply to different crop species is attributable to differences in the K requirements, water flux rates and to the differences in root parameters.