The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform
Shouyang Liu - One of the best experts on this subject based on the ideXlab platform.
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estimates of Plant Density of wheat crops at emergence from very low altitude uav imagery
Remote Sensing of Environment, 2017Co-Authors: Xiuliang Jin, Shouyang Liu, Frederic Baret, Matthieu Hemerle, Alexis ComarAbstract:Abstract 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 24 Mpixels and equipped with a 60 mm focal length lens was flying aboard an hexacopter at 3 to 7 m altitude at about 1 m/s speed. This allows getting ground resolution between 0.20 mm to 0.45 mm, 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 Greoux, 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/m 2 and 14.31% with a bias of 9.01 Plants/m 2 . 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 0.40 mm. 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.
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estimation of wheat Plant Density at early stages using high resolution imagery
Frontiers in Plant Science, 2017Co-Authors: Shouyang Liu, Bruno Andrieu, Philippe Burger, Fred Baret, Matthieu HemmerleAbstract: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 3 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.
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A method to estimate Plant Density and Plant spacing heterogeneity: application to wheat crops
Plant Methods, 2017Co-Authors: Shouyang Liu, Frederic Baret, Denis Allard, Xiuliang Jin, Bruno Andrieu, Philippe Burger, Matthieu Hemmerle, Alexis ComarAbstract:Background: Plant Density and its non-uniformity drive the competition among Plants as well as with weeds. They need thus to be estimated with small uncertainties accuracy. An optimal sampling method is proposed to estimate the Plant Density in wheat crops from Plant counting and reach a given precision. Results: Three experiments were conducted in 2014 resulting in 14 plots across varied sowing Density, cultivars and environmental conditions. The coordinates of the Plants along the row were measured over RGB high resolution images taken from the ground level. Results show that the spacing between consecutive Plants along the row direction are independent and follow a gamma distribution under the varied conditions experienced. A gamma count model was then derived to define the optimal sample size required to estimate Plant Density for a given precision. Results suggest that measuring the length of segments containing 90 Plants will achieve a precision better than 10%, independently from the Plant Density. This approach appears more efficient than the usual method based on fixed length segments where the number of Plants are counted: the optimal length for a given precision on the Density estimation will depend on the actual Plant Density. The gamma count model parameters may also be used to quantify the heterogeneity of Plant spacing along the row by exploiting the variability between replicated samples. Results show that to achieve a 10% precision on the estimates of the 2 parameters of the gamma model, 200 elementary samples corresponding to the spacing between 2 consecutive Plants should be measured. Conclusions: This method provides an optimal sampling strategy to estimate the Plant Density and quantify the Plant spacing heterogeneity along the row.
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Estimates of Plant Density of wheat crops at emergence from very low altitude UAV imagery
Remote Sensing of Environment, 2017Co-Authors: Xiuliang Jin, Shouyang Liu, Frederic Baret, Matthieu Hemerle, Alexis ComarAbstract: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 alpha 5100L RGB camera with 24 Mpixels and equipped with a 60 mm focal length lens was flying aboard an hexacopter at 3 to 7 m altitude at about 1 m/s speed. This allows getting ground resolution between 0.20 mm to 0.45 mm, while providing 59-77% overlap between images. The camera was looking with 45 degrees 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 Greoux, 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/m(2) and 14.31% with a bias of 9.01 Plants/m(2). 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 0.40 mm. 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.
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Estimation of Wheat Plant Density at Early Stages Using High Resolution Imagery
Frontiers in Plant Science, 2017Co-Authors: Shouyang Liu, Frederic Baret, Bruno Andrieu, Philippe Burger, Matthieu HemmerleAbstract: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.
Bruno Andrieu - One of the best experts on this subject based on the ideXlab platform.
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Architectural response of wheat cultivars to row spacing reveals altered perception of Plant Density
Frontiers in Plant Science, 2019Co-Authors: Mariem Abichou, Benoît De Solan, Bruno AndrieuAbstract:Achieving novel improvements in crop management may require changing interrow distance in cultivated fields. Such changes would benefit from a better understanding of Plant responses to the spatial heterogeneity in their environment. Our work investigates the architectural plasticity of wheat Plants in response to increasing row spacing and evaluates the hypothesis of a foraging behavior in response to neighboring Plants. A field experiment was conducted with five commercial winter wheat cultivars possessing unique architectures, grown under narrow (NI, 17.5 cm) or wide interrows (WI, 35 cm) at the same population Density (170 seeds/m2). We characterized the development (leaf emergence, tillering), the morphology (dimension of organs, leaf area index), and the geometry (ground cover, leaf angle, organ spreading, and orientation). All cultivars showed a lower number of emerged tillers in WI compared to NI, which was later compensated by lower tiller mortality and by shoots producing larger blades. The rate of leaf emergence and the final leaf number were higher in WI compared to NI, except for one cultivar. Around the start of stem elongation, pseudo-stems were more erect in WI, while around the time of flowering, stems were more inclined and leaves were more planophile. Cultivars differed in their degrees of responses, with one appearing to prospect more specifically within the interrow space in WI treatment. Altogether, our results suggest that altering interrow distance leads to changes in the perceived extent of competition by Plants, with responses first mimicking the effect of a higher Plant Density and later the effect of a lower Plant Density. Only one cultivar showed responses that suggested a perception of the heterogeneity of the environment. These findings improve our understanding of Plant responses to spatial heterogeneity and provide novel information to simulate light capture in Plant 3D models, depending on cultivar behavior.
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estimation of wheat Plant Density at early stages using high resolution imagery
Frontiers in Plant Science, 2017Co-Authors: Shouyang Liu, Bruno Andrieu, Philippe Burger, Fred Baret, Matthieu HemmerleAbstract: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 3 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.
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A method to estimate Plant Density and Plant spacing heterogeneity: application to wheat crops
Plant Methods, 2017Co-Authors: Shouyang Liu, Frederic Baret, Denis Allard, Xiuliang Jin, Bruno Andrieu, Philippe Burger, Matthieu Hemmerle, Alexis ComarAbstract:Background: Plant Density and its non-uniformity drive the competition among Plants as well as with weeds. They need thus to be estimated with small uncertainties accuracy. An optimal sampling method is proposed to estimate the Plant Density in wheat crops from Plant counting and reach a given precision. Results: Three experiments were conducted in 2014 resulting in 14 plots across varied sowing Density, cultivars and environmental conditions. The coordinates of the Plants along the row were measured over RGB high resolution images taken from the ground level. Results show that the spacing between consecutive Plants along the row direction are independent and follow a gamma distribution under the varied conditions experienced. A gamma count model was then derived to define the optimal sample size required to estimate Plant Density for a given precision. Results suggest that measuring the length of segments containing 90 Plants will achieve a precision better than 10%, independently from the Plant Density. This approach appears more efficient than the usual method based on fixed length segments where the number of Plants are counted: the optimal length for a given precision on the Density estimation will depend on the actual Plant Density. The gamma count model parameters may also be used to quantify the heterogeneity of Plant spacing along the row by exploiting the variability between replicated samples. Results show that to achieve a 10% precision on the estimates of the 2 parameters of the gamma model, 200 elementary samples corresponding to the spacing between 2 consecutive Plants should be measured. Conclusions: This method provides an optimal sampling strategy to estimate the Plant Density and quantify the Plant spacing heterogeneity along the row.
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Estimation of Wheat Plant Density at Early Stages Using High Resolution Imagery
Frontiers in Plant Science, 2017Co-Authors: Shouyang Liu, Frederic Baret, Bruno Andrieu, Philippe Burger, Matthieu HemmerleAbstract: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.
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Characterization of Plant Density and distribution pattern of wheat crops using ground-based or UAV imagery
2016Co-Authors: Shouyang Liu, Frederic Baret, Denis Allard, Xiuliang Jin, Bruno Andrieu, Alexis ComarAbstract:Plant Density is governed by the sowing Density and the emergence rate. For a given Plant Density, the uniformity of Plant distribution may significantly impact the competition between Plants as well as weeds. Plant Density and uniformity is therefore a key factor explaining production, although a number of species are able to compensate low Plant densities by a larger development of individual Plants during the growth cycle. However, as the lack of dedicated device, manual field counting in wheat crops is still extensively employed as the reference method although tedious and time consuming. Further, very little work has been devoted to document the Plant distribution pattern along the row. The main objective of this work is to propose a high-throughput method to estimate Plant Density and the distribution pattern from high resolution images taken at the emergence stage. Wheat was selected as the material because it is one of the major crops cultivated over the world with relatively high Plant Density (150-400 seeds·m-2). High-resolution RGB images were captured from ground-based or unmanned aerial vehicle (UAV) platforms over several wheat experiments in France. They covered a large range of densities from 100 to 600 Plants·m-2 over various cultivars at Haun stage around 1.5. Computer vision techniques were exploited to develop a pipeline for assessing the Plant Density and the distribution patterns of Plant positions along the row. The method provides a good estimation of the actual Plant Density with R2» 0.95 and rRMSE » 12% over ground-based imagery while performances degrade only slightly for the UAV imagery with R2» 0.86 and rRMSE »19%. The inter-Plant spacing along the row direction was found following a gamma distribution and the positions of the successive Plants are independent from each other. The distribution parameters may be retrieved from the images, subsequently characterizing the uniformity of Plant distribution along the row direction.
Alexis Comar - One of the best experts on this subject based on the ideXlab platform.
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estimates of Plant Density of wheat crops at emergence from very low altitude uav imagery
Remote Sensing of Environment, 2017Co-Authors: Xiuliang Jin, Shouyang Liu, Frederic Baret, Matthieu Hemerle, Alexis ComarAbstract:Abstract 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 24 Mpixels and equipped with a 60 mm focal length lens was flying aboard an hexacopter at 3 to 7 m altitude at about 1 m/s speed. This allows getting ground resolution between 0.20 mm to 0.45 mm, 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 Greoux, 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/m 2 and 14.31% with a bias of 9.01 Plants/m 2 . 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 0.40 mm. 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.
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A method to estimate Plant Density and Plant spacing heterogeneity: application to wheat crops
Plant Methods, 2017Co-Authors: Shouyang Liu, Frederic Baret, Denis Allard, Xiuliang Jin, Bruno Andrieu, Philippe Burger, Matthieu Hemmerle, Alexis ComarAbstract:Background: Plant Density and its non-uniformity drive the competition among Plants as well as with weeds. They need thus to be estimated with small uncertainties accuracy. An optimal sampling method is proposed to estimate the Plant Density in wheat crops from Plant counting and reach a given precision. Results: Three experiments were conducted in 2014 resulting in 14 plots across varied sowing Density, cultivars and environmental conditions. The coordinates of the Plants along the row were measured over RGB high resolution images taken from the ground level. Results show that the spacing between consecutive Plants along the row direction are independent and follow a gamma distribution under the varied conditions experienced. A gamma count model was then derived to define the optimal sample size required to estimate Plant Density for a given precision. Results suggest that measuring the length of segments containing 90 Plants will achieve a precision better than 10%, independently from the Plant Density. This approach appears more efficient than the usual method based on fixed length segments where the number of Plants are counted: the optimal length for a given precision on the Density estimation will depend on the actual Plant Density. The gamma count model parameters may also be used to quantify the heterogeneity of Plant spacing along the row by exploiting the variability between replicated samples. Results show that to achieve a 10% precision on the estimates of the 2 parameters of the gamma model, 200 elementary samples corresponding to the spacing between 2 consecutive Plants should be measured. Conclusions: This method provides an optimal sampling strategy to estimate the Plant Density and quantify the Plant spacing heterogeneity along the row.
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Estimates of Plant Density of wheat crops at emergence from very low altitude UAV imagery
Remote Sensing of Environment, 2017Co-Authors: Xiuliang Jin, Shouyang Liu, Frederic Baret, Matthieu Hemerle, Alexis ComarAbstract: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 alpha 5100L RGB camera with 24 Mpixels and equipped with a 60 mm focal length lens was flying aboard an hexacopter at 3 to 7 m altitude at about 1 m/s speed. This allows getting ground resolution between 0.20 mm to 0.45 mm, while providing 59-77% overlap between images. The camera was looking with 45 degrees 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 Greoux, 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/m(2) and 14.31% with a bias of 9.01 Plants/m(2). 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 0.40 mm. 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.
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Characterization of Plant Density and distribution pattern of wheat crops using ground-based or UAV imagery
2016Co-Authors: Shouyang Liu, Frederic Baret, Denis Allard, Xiuliang Jin, Bruno Andrieu, Alexis ComarAbstract:Plant Density is governed by the sowing Density and the emergence rate. For a given Plant Density, the uniformity of Plant distribution may significantly impact the competition between Plants as well as weeds. Plant Density and uniformity is therefore a key factor explaining production, although a number of species are able to compensate low Plant densities by a larger development of individual Plants during the growth cycle. However, as the lack of dedicated device, manual field counting in wheat crops is still extensively employed as the reference method although tedious and time consuming. Further, very little work has been devoted to document the Plant distribution pattern along the row. The main objective of this work is to propose a high-throughput method to estimate Plant Density and the distribution pattern from high resolution images taken at the emergence stage. Wheat was selected as the material because it is one of the major crops cultivated over the world with relatively high Plant Density (150-400 seeds·m-2). High-resolution RGB images were captured from ground-based or unmanned aerial vehicle (UAV) platforms over several wheat experiments in France. They covered a large range of densities from 100 to 600 Plants·m-2 over various cultivars at Haun stage around 1.5. Computer vision techniques were exploited to develop a pipeline for assessing the Plant Density and the distribution patterns of Plant positions along the row. The method provides a good estimation of the actual Plant Density with R2» 0.95 and rRMSE » 12% over ground-based imagery while performances degrade only slightly for the UAV imagery with R2» 0.86 and rRMSE »19%. The inter-Plant spacing along the row direction was found following a gamma distribution and the positions of the successive Plants are independent from each other. The distribution parameters may be retrieved from the images, subsequently characterizing the uniformity of Plant distribution along the row direction.
Martin M. Williams - One of the best experts on this subject based on the ideXlab platform.
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understanding variability in optimum Plant Density and recommendation domains for crowding stress tolerant processing sweet corn
PLOS ONE, 2020Co-Authors: Daljeet S. Dhaliwal, Martin M. WilliamsAbstract:Recent research shows significant economic benefit if the processing sweet corn [Zea mays L. var. rugosa (or saccharata)] industry grew crowding stress tolerant (CST) hybrids at their optimum Plant densities, which may exceed current Plant densities by up to 14,500 Plants ha-1. However, optimum Plant Density of individual fields varies over years and across the Upper Midwest (Illinois, Minnesota and Wisconsin), where processing sweet corn is concentrated. The objectives of this study were to: (1) determine the extent to which environmental and management practices affect optimum Plant Density and, (2) identify the most appropriate recommendation domain for making decisions on Plant Density. To capture spatial and temporal variability in optimum Plant Density, on-farm experiments were conducted at thirty fields across the states of Illinois, Minnesota and Wisconsin, from 2013 to 2017. Exploratory factor analysis of twelve environmental and management variables revealed two factors, one related to growing period and the other defining soil type, which explained the maximum variability observed across all the fields. These factors were then used to quantify the strength of associations with optimum Plant Density. Pearson's partial correlation coefficients of 'growing period' and 'soil type' with optimum Plant Density were low (ρ1 = -0.14 and ρ2 = -0.09, respectively) and non-significant (P = 0.47 and 0.65, respectively). To address the second objective, six candidate recommendation domain models (RDM) were developed and tested. Linear mixed effects models describing crop response to Plant Density were fit to each level of each candidate RDM. The difference in profitability observed at the current Plant Density for a field and the optimum Plant Density under RDM level represented the additional processor profit ($ ha-1) from a field. The RDM built around 'Production Area' (RDMPA) appears most suitable, because Plant Density recommendations based on RDMPA maximized processor profits as well grower returns better than other RDMs. Compared to current Plant Density, processor profits and grower returns increased by $448 ha-1 and $82 ha-1, respectively at Plant densities under RDMPA.
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Economically optimal Plant Density for machine-harvested edamame
HortScience, 2020Co-Authors: Daljeet S. Dhaliwal, Martin M. WilliamsAbstract:Consumer demand for edamame [Glycine max (L.) Merr.], the vegetable version of soybean (Glycine max), has grown during the past decade in North America. Domestic production of edamame is on the rise; however, research to guide fundamental crop production practices, including knowledge useful for developing appropriate recommendations for crop seeding rate, is lacking. Field experiments near Urbana, IL, were used to quantify edamame response to Plant Density and determine the economically optimal Plant Density (EOPD) of machine-harvested edamame. Crop growth and yield responses to a range of Plant densities (24,700 to 395,100 Plants/ha) were quantified in four edamame cultivars (AGS 292, BeSweet 292, Gardensoy 42, and Midori Giant) across 2 years. Plots were harvested with the Oxbo BH100, a fresh market bean harvester. In general, as Plant Density increased, branch number and the ratio of pod mass to vegetative mass decreased, while Plant height and leaf area index increased. Recovery, the percent of marketable pods in the machine-harvested sample, varied among cultivars from 86% to 95%. Results identified the EOPD for machine-harvested edamame ranged from 87,000 to 120,000 Plants/ha. When considering the effect of Plant Density on Plant morphology, as well as seeding cost, harvester efficiency, recovery, and marketable pod yield, edamame EOPDs are considerably lower than seeding rates of up to 344,200 seeds/ha recommended in recent publications.
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Optimum Plant Density for crowding stress tolerant processing sweet corn.
PloS one, 2019Co-Authors: Daljeet S. Dhaliwal, Martin M. WilliamsAbstract:Globally, gains in sweet corn [Zea mays L.var. rugosa (or saccharata)] are a fraction of the yield advances made in field corn (Zea mays L.) in the last half-century. Grain yield improvement of field corn is associated with increased tolerance to higher Plant densities (i.e., crowding stress). Processing sweet corn hybrids that tolerate crowding stress have been identified; however, such hybrids appear to be under-Planted in the processing sweet corn. Using crowding stress tolerant (CST) hybrids, the objectives of this study were to: (1) identify optimum Plant densities for a range of growing conditions; (2) quantify gaps in production between current and optimum Plant densities; and (3) enumerate changes in yield and ear traits when shifting from current to optimum Plant densities. Using a CST shrunken-2 (sh2) processing sweet corn hybrid, on-farm Plant Density trials were conducted in thirty fields across the states of Illinois, Minnesota and Wisconsin, from 2013 to 2017 in order to capture a wide variety of growing conditions. Linear mixed-effects models were used to identify the optimum Plant Density corresponding to maximum ear mass (Mt ha-1), case production (cases ha-1), and profitability to the processor ($ ha-1). Kernel moisture, indicative of Plant development, was unaffected by Plant Density. Ear traits, such as ear number and ear mass per Plant, average ear length, and filled ear length declined linearly with increasing Plant Density. Nonetheless, there was a large economic benefit to the grower and processor by shifting to higher Plant densities in most environments. This research shows that increasing Plant densities of CST hybrids from current (58,475 Plants ha-1) to optimum (73,075 Plants ha-1) could improve processing sweet corn green ear yield and processor profitability on average of 1.13 Mt ha-1 and $525 ha-1, respectively.
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Reproductive Sink of Sweet Corn in Response to Plant Density and Hybrid
HortScience, 2018Co-Authors: Martin M. WilliamsAbstract:Improvements in Plant Density tolerance have played an essential role in grain corn yield gains for ≈80 years; however, Plant Density effects on sweet corn biomass allocation to the ear (the reproductive ‘sink’) is poorly quantified. Moreover, optimal Plant densities for modern white-kernel shrunken-2 (sh2) hybrids are unknown. The objectives of the study were to 1) quantify the effect of Plant Density and hybrid on the reproductive sink of sweet corn and 2) determine optimal Plant densities for white-kernel sh2 sweet corn. Field experiments were conducted across 2 years on 10 white-kernel sh2 hybrids grown at Plant densities ranging from 4.3 to 8.6 Plants/m2. Increasing Plant Density negatively influenced reproductive sink characteristics of individual sweet corn Plants, including linear decreases in ear shoots/Plant, marketable ears/Plant, ear length, filled ear length, ear mass/Plant, and kernel mass/Plant. Reproductive traits varied widely among hybrids, including ear mass (15.6–20.6 Mt·ha−1) and recovery (32.3% to 42.4%), which is the contribution of fresh kernel mass to total ear mass. Hybrids had a common response to Plant Density, whereby ear yield was optimized at 5.5 Plants/m2 and gross profit margin was optimized at 6.1 Plants/m2. Plant Density data from 586 growers’ fields suggest current seeding rates have optimized the reproductive sink size for today’s white-kernel sh2 hybrids. However, room exists for improving Plant Density tolerance, yield, and profitability.
Matthieu Hemmerle - One of the best experts on this subject based on the ideXlab platform.
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estimation of wheat Plant Density at early stages using high resolution imagery
Frontiers in Plant Science, 2017Co-Authors: Shouyang Liu, Bruno Andrieu, Philippe Burger, Fred Baret, Matthieu HemmerleAbstract: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 3 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.
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A method to estimate Plant Density and Plant spacing heterogeneity: application to wheat crops
Plant Methods, 2017Co-Authors: Shouyang Liu, Frederic Baret, Denis Allard, Xiuliang Jin, Bruno Andrieu, Philippe Burger, Matthieu Hemmerle, Alexis ComarAbstract:Background: Plant Density and its non-uniformity drive the competition among Plants as well as with weeds. They need thus to be estimated with small uncertainties accuracy. An optimal sampling method is proposed to estimate the Plant Density in wheat crops from Plant counting and reach a given precision. Results: Three experiments were conducted in 2014 resulting in 14 plots across varied sowing Density, cultivars and environmental conditions. The coordinates of the Plants along the row were measured over RGB high resolution images taken from the ground level. Results show that the spacing between consecutive Plants along the row direction are independent and follow a gamma distribution under the varied conditions experienced. A gamma count model was then derived to define the optimal sample size required to estimate Plant Density for a given precision. Results suggest that measuring the length of segments containing 90 Plants will achieve a precision better than 10%, independently from the Plant Density. This approach appears more efficient than the usual method based on fixed length segments where the number of Plants are counted: the optimal length for a given precision on the Density estimation will depend on the actual Plant Density. The gamma count model parameters may also be used to quantify the heterogeneity of Plant spacing along the row by exploiting the variability between replicated samples. Results show that to achieve a 10% precision on the estimates of the 2 parameters of the gamma model, 200 elementary samples corresponding to the spacing between 2 consecutive Plants should be measured. Conclusions: This method provides an optimal sampling strategy to estimate the Plant Density and quantify the Plant spacing heterogeneity along the row.
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Estimation of Wheat Plant Density at Early Stages Using High Resolution Imagery
Frontiers in Plant Science, 2017Co-Authors: Shouyang Liu, Frederic Baret, Bruno Andrieu, Philippe Burger, Matthieu HemmerleAbstract: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.