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Termes descripteurs IGN > sciences humaines et sociales > économie > macroéconomie > secteur primaire > agriculture > agronomie > cultures > céréales > maïs (céréale)
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Stem-leaf segmentation and phenotypic trait extraction of individual maize using terrestrial LiDAR data / Shichao Jin in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)
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Titre : Stem-leaf segmentation and phenotypic trait extraction of individual maize using terrestrial LiDAR data Type de document : Article/Communication Auteurs : Shichao Jin, Auteur ; Yanjun Su, Auteur ; Fangfang Wu, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 1336 - 1346 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes descripteurs IGN] données lidar
[Termes descripteurs IGN] données localisées 3D
[Termes descripteurs IGN] maïs (céréale)
[Termes descripteurs IGN] phénologie
[Termes descripteurs IGN] segmentation par croissance de régionsRésumé : (Auteur) Accurate and high throughput extraction of crop phenotypic traits, as a crucial step of molecular breeding, is of great importance for yield increasing. However, automatic stem-leaf segmentation as a prerequisite of many precise phenotypic trait extractions is still a big challenge. Current works focus on the study of the 2-D image-based segmentation, which are sensitive to illumination and occlusion. Light detection and ranging (LiDAR) can obtain accurate 3-D information with its active laser scanning and strong penetration ability, which breaks through phenotyping from 2-D to 3-D. However, few researches have addressed the problem of the LiDAR-based stem-leaf segmentation. In this paper, we proposed a median normalized-vector growth (MNVG) algorithm, which can segment stem and leaf with four steps, i.e., preprocessing, stem growth, leaf growth, and postprocessing. The MNVG method was tested by 30 maize samples with different heights, compactness, leaf numbers, and densities from three growing stages. Moreover, phenotypic traits at leaf, stem, and individual levels were extracted with the truly segmented instances. The mean accuracy of segmentation at point level in terms of the recall, precision, F-score, and overall accuracy were 0.92, 0.93, 0.92, and 0.93, respectively. The accuracy of phenotypic trait extraction in leaf, stem, and individual levels ranged from 0.81 to 0.95, 0.64 to 0.97, and 0.96 to 1, respectively. To our knowledge, this paper proposed the first LiDAR-based stem-leaf segmentation and phenotypic trait extraction method in agriculture field, which may contribute to the study of LiDAR-based plant phonemics and precise agriculture. Numéro de notice : A2019-114 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2866056 date de publication en ligne : 19/09/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2866056 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92454
in IEEE Transactions on geoscience and remote sensing > vol 57 n° 3 (March 2019) . - pp 1336 - 1346[article]Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform / Mohd Shahrimie Mohd Asaari in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)
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Titre : Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform Type de document : Article/Communication Auteurs : Mohd Shahrimie Mohd Asaari, Auteur ; Puneet Mishra ; Stien Mertens, Auteur ; Stijn Dhondt, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 121 - 138 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes descripteurs IGN] analyse spectrale
[Termes descripteurs IGN] image hyperspectrale
[Termes descripteurs IGN] maïs (céréale)
[Termes descripteurs IGN] mesure de similitude
[Termes descripteurs IGN] réflectance végétale
[Termes descripteurs IGN] signature spectrale
[Termes descripteurs IGN] similitude spectrale
[Termes descripteurs IGN] stress hydriqueRésumé : (Auteur) The potential of close-range hyperspectral imaging (HSI) as a tool for detecting early drought stress responses in plants grown in a high-throughput plant phenotyping platform (HTPPP) was explored. Reflectance spectra from leaves in close-range imaging are highly influenced by plant geometry and its specific alignment towards the imaging system. This induces high uninformative variability in the recorded signals, whereas the spectral signature informing on plant biological traits remains undisclosed. A linear reflectance model that describes the effect of the distance and orientation of each pixel of a plant with respect to the imaging system was applied. By solving this model for the linear coefficients, the spectra were corrected for the uninformative illumination effects. This approach, however, was constrained by the requirement of a reference spectrum, which was difficult to obtain. As an alternative, the standard normal variate (SNV) normalisation method was applied to reduce this uninformative variability.
Once the envisioned illumination effects were eliminated, the remaining differences in plant spectra were assumed to be related to changes in plant traits. To distinguish the stress-related phenomena from regular growth dynamics, a spectral analysis procedure was developed based on clustering, a supervised band selection, and a direct calculation of a spectral similarity measure against a reference. To test the significance of the discrimination between healthy and stressed plants, a statistical test was conducted using a one-way analysis of variance (ANOVA) technique.
The proposed analysis techniques was validated with HSI data of maize plants (Zea mays L.) acquired in a HTPPP for early detection of drought stress in maize plant. Results showed that the pre-processing of reflectance spectra with the SNV effectively reduces the variability due to the expected illumination effects. The proposed spectral analysis method on the normalized spectra successfully detected drought stress from the third day of drought induction, confirming the potential of HSI for drought stress detection studies and further supporting its adoption in HTPPP.Numéro de notice : A2018-122 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.02.003 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.02.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89570
in ISPRS Journal of photogrammetry and remote sensing > vol 138 (April 2018) . - pp 121 - 138[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2018041 RAB Revue Centre de documentation En réserve 3L Disponible 081-2018043 DEP-EXM Revue MATIS Dépôt en unité Exclu du prêt 081-2018042 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications / Amanda Veloso in Remote sensing of environment, vol 199 (15 September 2017)
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Titre : Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications Type de document : Article/Communication Auteurs : Amanda Veloso, Auteur ; Stéphane Mermoz, Auteur ; Alexandre Bouvet, Auteur ; Thuy Le Toan, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 415 - 426 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes descripteurs IGN] blé (céréale)
[Termes descripteurs IGN] cultures
[Termes descripteurs IGN] Glycine max
[Termes descripteurs IGN] image optique
[Termes descripteurs IGN] image Sentinel-MSI
[Termes descripteurs IGN] image Sentinel-SAR
[Termes descripteurs IGN] maïs (céréale)
[Termes descripteurs IGN] Normalized Difference Vegetation Index
[Termes descripteurs IGN] surveillance agricole
[Termes descripteurs IGN] tournesol
[Termes descripteurs IGN] variation saisonnière
[Termes descripteurs IGN] variation temporelleRésumé : (auteur) Crop monitoring information is essential for food security and to improve our understanding of the role of agriculture on climate change, among others. Remotely sensing optical and radar data can help to map crop types and to estimate biophysical parameters, especially with the availability of an unprecedented amount of free Sentinel data within the Copernicus programme. These datasets, whose continuity is guaranteed up to decades, offer a unique opportunity to monitor crops systematically every 5 to 10 days. Before developing operational monitoring methods, it is important to understand the temporal variations of the remote sensing signal of different crop types in a given region. In this study, we analyse the temporal trajectory of remote sensing data for a variety of winter and summer crops that are widely cultivated in the world (wheat, rapeseed, maize, soybean and sunflower). The test region is in southwest France, where Sentinel-1 data have been acquired since 2014. Because Sentinel-2 data were not available for this study, optical satellites similar to Sentinel-2 are used, mainly to derive NDVI, for a comparison between the temporal behaviors with radar data. The SAR backscatter and NDVI temporal profiles of fields with varied management practices and environmental conditions are interpreted physically. Key findings from this analysis, leading to possible applications of Sentinel-1 data, with or without the conjunction of Sentinel-2, are then described. This study points out the interest of SAR data and particularly the VH/VV ratio, which is poorly documented in previous studies. Numéro de notice : A2017-418 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : https://doi.org/10.1016/j.rse.2017.07.015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86311
in Remote sensing of environment > vol 199 (15 September 2017) . - pp 415 - 426[article]Télédétection pour l'agriculture de précision par caméra hyperspectrale miniature / D. Constantin in Géomatique suisse, vol 113 n° 9 (septembre 2015)
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Titre : Télédétection pour l'agriculture de précision par caméra hyperspectrale miniature Type de document : Article/Communication Auteurs : D. Constantin, Auteur ; Manuel Cubero-Castan, Auteur ; Y. Akhtman, Auteur ; Bertrand Merminod, Auteur Année de publication : 2015 Article en page(s) : pp 340 - 344 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes descripteurs IGN] acquisition d'images
[Termes descripteurs IGN] agriculture de précision
[Termes descripteurs IGN] analyse de données
[Termes descripteurs IGN] Brésil
[Termes descripteurs IGN] capteur hyperspectral
[Termes descripteurs IGN] carte de la végétation
[Termes descripteurs IGN] données de terrain
[Termes descripteurs IGN] drone
[Termes descripteurs IGN] exploitation agricole
[Termes descripteurs IGN] image hyperspectrale
[Termes descripteurs IGN] maïs (céréale)Résumé : (Auteur) Un nouveau type de caméra hyperspectrale permet de miniaturiser le système d'acquisition de données, qui peut être installé sur un drone de taille modeste. Dès lors, cette technologie devient accessible au grand public pour de nombreuses applications telles que l'agriculture de précision. Cet article présente la méthodologie de la première campagne de mesure à large échelle sur une exploitation agricole produisant du maïs dans la région centrale du Brésil, ainsi que les premiers résultats. Numéro de notice : A2015-551 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77585
in Géomatique suisse > vol 113 n° 9 (septembre 2015) . - pp 340 - 344[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 136-2015091 SL Revue Centre de documentation Revues en salle Disponible Impact of diurnal variation in vegetation water content on radar backscatter from maize during water stress / Tim Van Emmerik in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)
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Titre : Impact of diurnal variation in vegetation water content on radar backscatter from maize during water stress Type de document : Article/Communication Auteurs : Tim Van Emmerik, Auteur ; Susan C. Steele-Dunne, Auteur ; Jasmeet Judge, Auteur ; Nick Van De Giesen, Auteur Année de publication : 2015 Article en page(s) : pp 3855 - 3869 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes descripteurs IGN] coefficient de rétrodiffusion
[Termes descripteurs IGN] image radar
[Termes descripteurs IGN] maïs (céréale)
[Termes descripteurs IGN] teneur en eau de la végétation
[Termes descripteurs IGN] végétationRésumé : (Auteur) Microwave backscatter from vegetated surfaces is influenced by vegetation structure and vegetation water content (VWC), which varies with meteorological conditions and moisture in the root zone. Radar backscatter observations are used for many vegetation and soil moisture monitoring applications under the assumption that VWC is constant on short timescales. This research aims to understand how backscatter over agricultural canopies changes in response to diurnal differences in VWC due to water stress. A standard water-cloud model and a two-layer water-cloud model for maize were used to simulate the influence of the observed variations in bulk/leaf/stalk VWC and soil moisture on the various contributions to total backscatter at a range of frequencies, polarizations, and incidence angles. The bulk VWC and leaf VWC were found to change up to 30% and 40%, respectively, on a diurnal basis during water stress and may have a significant effect on radar backscatter. Total backscatter time series are presented to illustrate the simulated diurnal difference in backscatter for different radar frequencies, polarizations, and incidence angles. Results show that backscatter is very sensitive to variations in VWC during water stress, particularly at large incidence angles and higher frequencies. The diurnal variation in total backscatter was dominated by variations in leaf water content, with simulated diurnal differences of up to 4 dB in X- through Ku-bands (8.6-35 GHz) . This study highlights a potential source of error in current vegetation and soil monitoring applications and provides insights into the potential use for radar to detect variations in VWC due to water stress. Numéro de notice : A2015-314 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76561
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 7 (July 2015) . - pp 3855 - 3869[article]Réservation
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A2015-314_Impact of diurnal variation in vegetation water contentHTML text data (RFC 1866)Effect of corn on C-an L-band radar backscatter: a correction method for soil moisture retrieval / A. Joseph in Remote sensing of environment, vol 114 n° 11 (15/11/2010)
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PermalinkIntegration of linear programming and a watershed-scale hydrologic model for proposing an optimized land-use and assessing its impact on soil conservation: a case study of the Nagwan watershed in the Hazaribagh district of Jharkhand, India / R. Kaur in International journal of geographical information science IJGIS, vol 18 n° 1 (january - february 2004)
PermalinkApplication of linear mixture model on low resolution NDVI time series of Spot Vegetation data for improving crop yield forecasting techniques in Sinaloa region of Mexico / A. Quadri (2004)
PermalinkVegetation canopy anisotropy at 1.4 GHz / B.K. Hornbuckle in IEEE Transactions on geoscience and remote sensing, vol 41 n° 10 (October 2003)
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