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 emergen...

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Bibliographic Details
Published in:	Remote sensing of environment Vol. 198; pp. 105 - 114
Main Authors:	Jin, Xiuliang, Liu, Shouyang, Baret, Frédéric, Hemerlé, Matthieu, Comar, Alexis
Format:	Journal Article
Language:	English
Published:	Elsevier Inc 01.09.2017 Elsevier
Subjects:	altitude cameras computer software Computer vision algorithm crops developmental stages Environmental Sciences flight genotype Global Changes leaves Particle swarm optimization (PSO)-support vector machine (SVM) phenotype Plant density remote sensing rotors sowing date support vector machines Unmanned aerial vehicle wheat wind Winter wheat Computer vision algorithm Particle swarm optimization (PSO)-support vector machine (SVM) Unmanned aerial vehicle Plant density Winter wheat densité de végétation drone optimisation multi-objectifs par essaim particulaire (mopso) multi-objective particle swarm optimization (mopso) biodiversité végétale imagerie haute resolution culture agricole phénotypage wheat blé
ISSN:	0034-4257, 1879-0704
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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 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. •A method is presented to estimate wheat plant density from UAV RGB images.•Results show a RMSE is 34.05plants/m2 with a bias of 9.01plants/m2.•Plants should have between one to two leaves when images are taken.•The ground resolution of the images should be better than 0.40mm.
AbstractList	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. 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. 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. •A method is presented to estimate wheat plant density from UAV RGB images.•Results show a RMSE is 34.05plants/m2 with a bias of 9.01plants/m2.•Plants should have between one to two leaves when images are taken.•The ground resolution of the images should be better than 0.40mm.
Author	Hemerlé, Matthieu Comar, Alexis Jin, Xiuliang Liu, Shouyang Baret, Frédéric
Author_xml	– sequence: 1 givenname: Xiuliang surname: Jin fullname: Jin, Xiuliang email: xiuliang.jin@inra.fr, jinxiuxiuliang@126.com organization: UMR EMMAH, INRA, UAPV, 84914 Avignon, France – sequence: 2 givenname: Shouyang surname: Liu fullname: Liu, Shouyang organization: UMR EMMAH, INRA, UAPV, 84914 Avignon, France – sequence: 3 givenname: Frédéric surname: Baret fullname: Baret, Frédéric organization: UMR EMMAH, INRA, UAPV, 84914 Avignon, France – sequence: 4 givenname: Matthieu surname: Hemerlé fullname: Hemerlé, Matthieu organization: HIPHEN, 84914 Avignon, France – sequence: 5 givenname: Alexis surname: Comar fullname: Comar, Alexis organization: HIPHEN, 84914 Avignon, France
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Keywords	Computer vision algorithm Particle swarm optimization (PSO)-support vector machine (SVM) Unmanned aerial vehicle Plant density Winter wheat densité de végétation drone optimisation multi-objectifs par essaim particulaire (mopso) multi-objective particle swarm optimization (mopso) biodiversité végétale imagerie haute resolution culture agricole phénotypage wheat blé
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Snippet	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...
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SubjectTerms	altitude cameras computer software Computer vision algorithm crops developmental stages Environmental Sciences flight genotype Global Changes leaves Particle swarm optimization (PSO)-support vector machine (SVM) phenotype Plant density remote sensing rotors sowing date support vector machines Unmanned aerial vehicle wheat wind Winter wheat
Title	Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery
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