Search

Your search keyword '"Kamran Irfan"' showing total 10 results
Start Over Author "Kamran Irfan"
10 results on '"Kamran Irfan"'

1. VegAnn, Vegetation Annotation of multi-crop RGB images acquired under diverse conditions for segmentation

Author

Simon Madec, Kamran Irfan, Kaaviya Velumani, Frederic Baret, Etienne David, Gaetan Daubige, Lucas Bernigaud Samatan, Mario Serouart, Daniel Smith, Chrisbin James, Fernando Camacho, Wei Guo, Benoit De Solan, Scott C. Chapman, and Marie Weiss

Subjects
Science
Abstract

Abstract Applying deep learning to images of cropping systems provides new knowledge and insights in research and commercial applications. Semantic segmentation or pixel-wise classification, of RGB images acquired at the ground level, into vegetation and background is a critical step in the estimation of several canopy traits. Current state of the art methodologies based on convolutional neural networks (CNNs) are trained on datasets acquired under controlled or indoor environments. These models are unable to generalize to real-world images and hence need to be fine-tuned using new labelled datasets. This motivated the creation of the VegAnn - Vegetation Annotation - dataset, a collection of 3775 multi-crop RGB images acquired for different phenological stages using different systems and platforms in diverse illumination conditions. We anticipate that VegAnn will help improving segmentation algorithm performances, facilitate benchmarking and promote large-scale crop vegetation segmentation research.

Published
2023
Full Text
View/download PDF

2. Assessment of Agricultural Practices From Sentinel 1 and 2 Images Applied on Rice Fields to Develop a Farm Typology in the Camargue Region

Author

Dominique Courault, Laure Hossard, Fabrice Flamain, Nicolas Baghdadi, and Kamran Irfan

Subjects
Agricultural practices, classification, farm typology, high resolution, remote sensing, rice, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809
Abstract

In the global change context, an efficient management of the available resources has become one of the most important topics particularly for the sustainable crop development. Many questions concern the evolution of the rice farming systems in Camargue in Southeastern France, which play a crucial role in controlling the soil salinity. Their surface area significantly decreased from 20 000 ha in 2010 to 14 000 ha in 2014. The arrival of the new Sentinel satellites makes it possible to evaluate these crop evolutions. The objectives of this study were to propose operational methodologies to accurately assess the surface areas of the main crops, rice, wheat, and grassland, from classifications based on multispectral data; map agricultural practices (sowing and harvest residue burning); and elaborate a farm typology based on variables computed from remote sensing data to better understand the farming strategies. Dense time series of Sentinel images acquired at high spatial resolution (10 m) were analyzed for 2016 and 2017. A satisfactory accuracy was obtained for land use classification with 88% of correctly classified fields. The accuracy obtained for the estimation of the sowing date varied according to the studied year from 8 to 12 days, and burned areas were correctly identified (80%). The farm typology allowed to cluster farms at the territory level.

Published
2020
Full Text
View/download PDF

4. Assessment of Agricultural Practices From Sentinel 1 and 2 Images Applied on Rice Fields to Develop a Farm Typology in the Camargue Region

Author

Laure Hossard, Kamran Irfan, Nicolas Baghdadi, Dominique Courault, Fabrice Flamain, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Innovation et Développement dans l'Agriculture et l'Alimentation (UMR Innovation), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and This work was supported by the French National Center of Spatial Studies (CNES project,funded by the TOSC Acomity) and by INRAE.

Subjects
Atmospheric Science, Soil salinity, 010504 meteorology & atmospheric sciences, Geophysics. Cosmic physics, [SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/Agronomy, 0211 other engineering and technologies, Context (language use), 02 engineering and technology, Agricultural engineering, 01 natural sciences, Farm typology, Grassland, Crop, High resolution, Computers in Earth Sciences, TC1501-1800, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, 2. Zero hunger, geography.geographical_feature_category, Land use, QC801-809, business.industry, Sowing, Remote sensing, 15. Life on land, Classification, Ocean engineering, Agricultural practices, Agricultural practice, Geography, Agriculture, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Paddy field, Rice, business
Abstract

International audience; In the global change context, an efficient management of the available resources has become one of the most important topics particularly for the sustainable crop development. Many questions concern the evolution of the rice farming systems in Camargue in Southeastern France, which play a crucial role in controlling the soil salinity. Their surface area significantly decreased from 20 000 ha in 2010 to 14 000 ha in 2014. The arrival of the new Sentinel satellites makes it possible to evaluate these crop evolutions. The objectives of this study were to propose operational methodologies to accurately assess the surface areas of the main crops, rice, wheat, and grassland, from classifications based on multispectral data; map agricultural practices (sowing and harvest residue burning); and elaborate a farm typology based on variables computed from remote sensing data to better understand the farming strategies. Dense time series of Sentinel images acquired at high spatial resolution (10 m) were analyzed for 2016 and 2017. A satisfactory accuracy was obtained for land use classification with 88% of correctly classified fields. The accuracy obtained for the estimation of the sowing date varied according to the studied year from 8 to 12 days, and burned areas were correctly identified (80%). The farm typology allowed to cluster farms at the territory level.

Published
2020
Full Text
View/download PDF

5. STICS crop model and Sentinel-2 images for monitoring rice growth and yield in the Camargue region

Author

Dominique Courault, Françoise Ruget, Laure Hossard, Valérie Demarez, Nicolas Baghdadi, Hélène Dechatre, Fabrice Flamain, Kamran Irfan, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Innovation et Développement dans l'Agriculture et l'Alimentation (UMR Innovation), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), CNES Centre national d'études spatiales (projet TOSCA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Production mapping, Environmental Engineering, 010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], [SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/Agronomy, 01 natural sciences, Yield (wine), Leaf area index, Paddy, 0105 earth and related environmental sciences, Remote sensing, 2. Zero hunger, Farm scale, business.industry, 04 agricultural and veterinary sciences, 15. Life on land, LAI, Remote sensing (archaeology), Agriculture, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Paddy field, Spatial variability, Scale (map), business, Agronomy and Crop Science, Cropping
Abstract

International audience; The assessment of rice yield at territory level is important for strategic economic decisions. Assessing spatial and temporal yield variability at regional scale is difficult because of the numerous factors involved, including agricultural practices, phenological calendars, and environmental contexts. New remote sensing data acquired at decametric resolution (Sentinel missions) can provide information on this spatial variability. The study objective was thus to evaluate the potential of Sentinel-2 images for monitoring rice cropping systems and yield from farm to region scales. The approach considered both observations and modeling. In-depth farmers surveys were carried out in the Camargue region, Southeastern France. The novelty was to use operational tools (BVNET and PHENOTB) to compute leaf area index, to daily interpolate this biophysical variable from 44 images acquired in 2016 and 2017 for each rice field, and to derive key phenological parameters from the analysis of the temporal profiles. The STICS crop model was spatially used, considering the biophysical variables derived from remote sensing. We tested four simulation strategies, differing in the integration intensity of remote sensing information into the model. Results have shown that (1) Sentinel-2 data allowed distinguishing early and late rice varieties. (2) The phenological stages mapped at the regional level allowed to better understand the agricultural practices of farmers. (3) The assimilation of remote sensing data to the STICS crop model significantly improved yield estimation and provided useful information on the spatial variability observed at regional scale. It was the first time that Sentinel-2 data are used with STICS crop model to assess rice yield at both farm and regional scale in the Camargue area. The proposed method is based on free open data and free access model, easily reproducible in other environmental contexts.

Published
2021
Full Text
View/download PDF

6. Leaf-rolling in maize crops: from leaf scoring to canopy-level measurements for phenotyping

Author

Sébastien Praud, Simon Madec, Marie Helene Tixier, Alexis Comar, Jeremy Lopez, Dan Dutartre, Matthieu Hemmerlé, Kamran Irfan, Frédéric Baret, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut National de la Recherche Agronomique (INRA)-Avignon Université (AU), BIOGEMMA, Institut National de la Recherche Agronomique (INRA), Programme d'investissement d'Avenir' PHENOME [ANR-11-INBS-012], and Programme d'investissement d'Avenir' PHENOME (ANR-11-INBS-012)

Subjects
0106 biological sciences, 0301 basic medicine, Canopy, genetic structures, Physiology, Plant Science, maize, Zea mays, 01 natural sciences, Spearman's rank correlation coefficient, FIPAR diurnal course, water stress, 03 medical and health sciences, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Desiccation, Photosynthesis, leaf rolling, digital hemispherical photographs, Mathematics, Morning, 2. Zero hunger, photoperiodism, fungi, Water stress, food and beverages, Research Papers, High-Throughput Screening Assays, Plant Leaves, Horticulture, Phenotype, 030104 developmental biology, Plant—Environment Interactions, Photosynthetically active radiation, Time course, Stage (hydrology), Canopy structure, 010606 plant biology & botany
Abstract

The diurnal dynamics of leaf-rolling in maize genotypes as measured by visual scoring are strongly correlated with corresponding measurements made using digital hemispherical photography, leading to the potential for development of high-throughput techniques for phenotyping in the field., Leaf rolling in maize crops is one of the main plant reactions to water stress that can be visually scored in the field. However, leaf-scoring techniques do not meet the high-throughput requirements needed by breeders for efficient phenotyping. Consequently, this study investigated the relationship between leaf-rolling scores and changes in canopy structure that can be determined by high-throughput remote-sensing techniques. Experiments were conducted in 2015 and 2016 on maize genotypes subjected to water stress. Leaf-rolling was scored visually over the whole day around the flowering stage. Concurrent digital hemispherical photographs were taken to evaluate the impact of leaf-rolling on canopy structure using the computed fraction of intercepted diffuse photosynthetically active radiation, FIPARdif. The results showed that leaves started to roll due to water stress around 09:00 h and leaf-rolling reached its maximum around 15:00 h (the photoperiod was about 05:00–20:00 h). In contrast, plants maintained under well-watered conditions did not show any significant rolling during the same day. A canopy-level index of rolling (CLIR) is proposed to quantify the diurnal changes in canopy structure induced by leaf-rolling. It normalizes for the differences in FIPARdif between genotypes observed in the early morning when leaves are unrolled, as well as for yearly effects linked to environmental conditions. Leaf-level rolling score was very strongly correlated with changes in canopy structure as described by the CLIR (r2=0.86, n=370). The daily time course of rolling was characterized using the amplitude of variation, and the rate and the timing of development computed at both the leaf and canopy levels. Results obtained from eight genotypes common between the two years of experiments showed that the amplitude of variation of the CLIR was the more repeatable trait (Spearman coefficient ρ=0.62) as compared to the rate (ρ=0.29) and the timing of development (ρ=0.33). The potential of these findings for the development of a high-throughput method for determining leaf-rolling based on aerial drone observations are considered.

Published
2018
Full Text
View/download PDF

7. Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model

Author

Mariem Abichou, Frédéric Boudon, Matthieu Hemmerlé, Kamran Irfan, Christian Fournier, Frédéric Baret, Kaiguang Zhao, Scott Thomas, Bruno Andrieu, Shouyang Liu, Benoit de Solan, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), ARVALIS - Institut du Végétal [Boigneville], ARVALIS - Institut du végétal [Paris], Ohio Agricultural Research and Development Ohio Agricultural Research and Development Center, The Ohio State University, Ohio State University [Columbus] (OSU), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Hiphen Integrated Plant Phenotyping Systems, AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Simulation et Analyse de la morphogenèse in siliCo (MOSAIC), Inria Grenoble - Rhône-Alpes, Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut National de la Recherche Agronomique (INRA)-Avignon Université (AU), 'Programme d’investissement d’Avenir' PHENOME (ANR-11-INBS-012) and Breedwheat (ANR-10-BTR-03), France Agrimer, 'Fonds de Soutien à l’Obtention Végétale', Chinese Scholarship Council (CSC)., ANR-11-INBS-0012,PHENOME,Centre français de phénomique végétale(2011), ANR-10-BTBR-0003,BREEDWHEAT,Développer de nouvelles variétés de blé pour une agriculture durable(2010), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)

Subjects
0106 biological sciences, Canopy, Atmospheric Science, LiDAR, 010504 meteorology & atmospheric sciences, Point cloud, F62 - Physiologie végétale - Croissance et développement, 01 natural sciences, ADEL-Wheat, Poisson model, 3D canopy structure model, Structured model, 0105 earth and related environmental sciences, Remote sensing, 2. Zero hunger, Global and Planetary Change, GAI, Artificial neural network, Forestry, Ranging, Green area index, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Lidar, Phenotyping, Wheat, Environmental science, U30 - Méthodes de recherche, Interception, Agronomy and Crop Science, Neural networks, 010606 plant biology & botany
Abstract

International audience; The use of active remote sensing techniques based on light detection and ranging (LiDAR) was investigated here to estimate the green area index (GAI) of wheat crops. Emphasis was put on the maximum GAI development stage when saturation effects are known to limit the performances of standard indirect methods based either on the gap fraction or reflectance measurements. The LiDAR provides both the three dimensional (3D) point cloud from which the vertical distribution (Z profile) of the interception points is computed, as well as the intensity of the returned signal from which the green fraction (GF) is derived. The data were interpreted by exploiting the 3D ADEL-Wheat model that synthesizes the knowledge accumulated on wheat canopy structure. A LiDAR simulator that accounts for the specific observation configuration used was developed to mimic the actual LiDAR measurements. The in-silico experiments were conducted to generate training and validation dataset. Neural network were then used to estimate GAI from the Z profile and GF derived from the LiDAR measurements. Performances of GAI estimates by the several methods investigated were evaluated using either experimental data with 3 < GAI < 6 and data simulated with the 3D structure model with 1 < GAI < 7. Results confirm that using only the GF provides poor estimates of GAI (0.89 < RMSE < 1.28; 0.22 < rRMSE < 0.31), regardless of turbid medium or realistic assumptions on canopy 3D structure. The introduction of the Z profile information improved significantly the GAI estimation accuracy (0.48 < RMSE < 0.55; 0.12 < rRMSE < 0.13). This study demonstrates the interest of using the third dimension provided by LiDAR to better estimate GAI in crops under high GAI values. However, this requires the use of a realistic 3D structure crop model over which the LiDAR data could be simulated under the observational configuration used.

Published
2017
Full Text
View/download PDF

8. Leaf rolling in maize crops: from leaf scoring to canopy level measurements for phenotyping

Author

Matthieu Hemmerlé, Dan Dutartre, Kamran Irfan, Jeremy Lopez, Frédéric Baret, Alexis Comar, Marie Helene Tixier, Simon Madec, and Sébastien Praud

Subjects
Canopy, Agronomy, Visual score, Hemispherical photography, Water stress, Plant biology, Mathematics
Abstract

Leaf rolling in maize crops is one of the main plant reactions to water stress that may be visually scored in the field. However, the leaf scoring did not reach the high-throughput desired by breeders for efficient phenotyping. This study investigates the relationship between leaf rolling score and the induced canopy structure changes that may be accessed by high-throughput remote sensing techniques.Results gathered over a field phenotyping platform run in 2015 and 2016 show that leaf starts to roll for the water stressed conditions around 9:00 and reaches its maximum around 15:00. Conversely, genotypes conducted under well watered conditions do not show any significant rolling during the same day. Leaf level rolling was very strongly correlated to canopy structure changes as described by the fraction of intercepted radiationfIPARWSderived from digital hemispherical photography. The changes infIPARWSwere stronly correlated (R2=0.86,n=50) to the leaf level rolling visual score. Further, a very good consistency of the genotype ranking of thefIPARWSchanges during the day was found (ρ=0.62). This study demonstrating the strong coordination between leaf level rolling and its impact on canopy structure changes poses the basis for new high-throughput remote sensing methods to quantify this water stress trait.HighlighThe diurnal dynamics of leaf rolling scored visually is strongly related to canopy structure changes that can be documented using Digital hemispherical photography. Consequences for high-throughput field phenotyping are discussed

Published
2017
Full Text
View/download PDF

9. Impact of 60 Years of Intensive Rice Cropping on Clay Minerals in Soils Due to Si Exportation

Author

Tanvir Shahzad, Guilhem Bourrié, Fabienne Trolard, Kamran Irfan, Jean-Claude Mouret, Lise Cary, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University of Faisalabad, Bureau de Recherches Géologiques et Minières (BRGM) (BRGM), Innovation et Développement dans l'Agriculture et l'Alimentation (UMR Innovation), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Irfan, Kamran, and Cary, Lise

Subjects
Silicon, smectite, Population, grain de riz, agriculture intensive, Fluvial, 02 engineering and technology, 020501 mining & metallurgy, Crop, [SDU.STU.GC]Sciences of the Universe [physics]/Earth Sciences/Geochemistry, culture du riz, Kaolinite, education, Crystallinity, 2. Zero hunger, sol argileux, education.field_of_study, Exportation, dynamique des minéraux, General Medicine, 15. Life on land, silice, 0205 materials engineering, Agronomy, Soil water, Environmental science, Paddy field, Clays, Rice, Clay minerals, rice, impact sur le milieu
Abstract

International audience; Rice is cultivated as staple for over half of the World's population. In Camargue (South of France) rice fields have been established on very young soils developed from historic fluvial deposits of the Rhône River. The comparison of clay mineralogy in a paddy field cultivated for 60 years and in a control shows a significant increase of the clay crystallinity in the paddy field soil, which implies a decrease of their solubility. In the paddy soils, phytoliths, poorly crystallized clays, such as smectite and to a lesser extent kaolinite, are progressively dissolved to supply Si for rice requirements. The sustainability of the crop system requires the clearing of silica exportations.

Published
2017
Full Text
View/download PDF

10. Describing the dynamics of wheat canopy structure at early stages by assimilating observations of green fractions within a 3D wheat model

Author

Shouyang Liu, Frederic Baret, Samuel Buis, Mariem Abichou, Bruno Andrieu, Kamran Irfan, Benoît de Solan, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Université Paris Saclay (COmUE), Station Expérimentale, and ARVALIS - Institut du végétal [Paris]

Subjects
[SDV]Life Sciences [q-bio], [SDE]Environmental Sciences
Abstract

International audience; Monitoring the dynamics of canopy architecture during the early stages is highly desirable for breeders to select cultivars of high drought-tolerance, resistance to weed infestation, improved water-use efficiency and potentially high grain yield. To achieve this objective, we propose to assimilate phenotyping observations into 3D canopy model. The feasibility and interest of this approach was evaluated through in silico experiments on wheat crops. A sequential assimilation scheme was developed to limit the space of canopy realization since the simulations of the 3D canopy architecture as well as the derivation of the corresponding phenotyping measurements is computer demanding. The early growth was decomposed into 2 consecutive stages. For each stage, 5 main influential parameters were identified. The green fraction measurements are estimated from RGB images simulated at several dates during the early stages and from several view inclination angles. The 5 influential parameters of the first phase and the 5 additional ones used for the second phase were estimated using a neural network machine learning technique with the GF temporal profiles as inputs. The posterior information derived after the first phase was used as prior information for the second phase of assimilation. Results show that the parameters defining the size and rate of change of the green wheat crop elements were estimated with a relatively good accuracy. Conversely, the parameters driving the orientation and inclination of the elements were retrieved with a poor accuracy. However, some important emerging properties were very well retrieved, including the GAI and the number of tillers with more than 3 leaves at the beginning of stem elongation. Further detailed sensitivity analysis highlights the importance of getting enough dates of measurements covering the growth cycle, while dates separated by less than a phyllochron do not provide significant additional information. The sensitivity analysis highlighted also the importance of the inclined observations. The assimilation approach proposed appears very general and could be applied to a number of phenotyping measurements completed along the growth cycle. Nevertheless, because our results were achieved using in silico experiments, further validation is necessary using actual field measurements.

Catalog

Books, media, physical & digital resources
See catalog results