Research Output

HIPHEN, in collaboration with other research organizations, is constantly involved in the development of new methods for varied applications such as plant counting, estimation of plant traits, leaf rolling, etc.

 

Following is an inventory of our team members' and UMT-CAPTE's research works published in scientific journals:

HIPHEN

 

Liu, S., Baret, F., Allard, D., Jin, X., Andrieu, B., Burger, P., ... & Comar, A. (2017). A method to estimate plant density and plant spacing heterogeneity: application to wheat crops. Plant Methods13(1), 38. doi: 10.1186/s13007-017-0187-1

 

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. 

 

 Jay, S., Gorretta, N., Morel, J., Maupas, F., Bendoula, R., Rabatel, G., ... & Baret, F. (2017). Estimating leaf chlorophyll content in sugar beet canopies using millimeter-to centimeter-scale reflectance imagery. Remote Sensing of Environment198, 173-186. https://doi.org/10.1016/j.rse.2017.06.008

 

Accurate estimation of leaf chlorophyll content (Cab) from remote sensing is of tremendous significance to monitor the physiological status of vegetation or to estimate primary production. Many vegetation indices (VIs) have been developed to retrieve Cab at the canopy level from meter- to decameter-scale reflectance observations. However, most of these VIs may be affected by the possible confounding influence of canopy structure. The objective of this study is to develop methods for Cab estimation using millimeter to centimeter spatial resolution reflectance imagery acquired at the field level.

 

 

 Jin, X., Liu, S., Baret, F., Hemmerlé, M., & Comar, A. (2017). Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sensing of Environment198, 105-114. https://doi.org/10.1016/j.rse.2017.06.007

 

The capacity of LiDAR and Unmanned Aerial Vehicles (UAVs) to provide plant height estimates as a high-throughput plant phenotyping trait was explored. An experiment over wheat genotypes conducted under well watered and water stress modalities was conducted. Finally, the capacity of plant height as a proxy for total above ground biomass and yield is discussed.

 

 Baret, F., Madec, S., Irfan, K., Lopez, J., Comar, A., Hemmerlé, M., ... & Tixier, M. H. (2017). Leaf rolling in maize crops: from leaf scoring to canopy level measurements for phenotyping. bioRxiv, 201665.  https://doi.org/10.1101/201665

 

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. 

 

Liu, S., Baret, F., Andrieu, B., Burger, P., & Hemmerle, M. (2017). Estimation of wheat plant density at early stages using high resolution imagery. Frontiers in plant science8. https://dx.doi.org/10.3389/fpls.2017.00739

 

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

 

 Liu, S., Baret, F., Boudon, F., Fournier, C., Andrieu, B., Abichou, M., Hemmerle, M., & De Solan, B. (2016, November). Estimating canopy characteristics from ground-based LiDAR measurement assisted with 3D Adel-Wheat model. In FSPMA2016, International Conference on Functional-Structural Plant Growth Modeling, Simulation, Visualization and Applications, IEEE (p. np).

 

Functional structural plant modeling (FSPM) integrates the physiological and morphological information from organizational scales to the canopy level. The combination of new phenotyping techniques with FSPMs is expected therefore to estimate a set of FSPMs parameters corresponding to traits of interest. LiDAR (Light Detection And Ranging) is recently exploited for detailed 3D description of the canopy structure, especially over dense canopies with small elements such as wheat and barley. In this work, we propose to use a model-assisted phenotyping approach to improve our understanding of the interaction between laser beam and canopy.

 

Gouache, D., Beauchêne, K., Mini, A., Fournier, A., De Solan, B., Baret, F., & Comar, A. (2016, June). Applying remote sensing expertise to crop improvement: progress and challenges to scale up high throughput field phenotyping from research to industry. In SPIE Commercial+ Scientific Sensing and Imaging (pp. 986604-986604). International Society for Optics and Photonics. http://dx.doi.org/10.1117/12.2229389

 

 Digital and image analysis technologies in greenhouses have become commonplace in plant science research and started to move into the plant breeding industry. However, the core of plant breeding work takes place in fields. We will present successive technological developments that have allowed the migration and application of remote sensing approaches at large into the field of crop genetics and physiology research, with a number of projects that have taken place in France.

 

 

 

UMT - CAPTE

  • Li, W., Baret, F., Weiss, M., Buis, S., Lacaze, R., Demarez, V., ... & Camacho, F. (2017). Combining hectometric and decametric satellite observations to provide near real time decametric FAPAR product. Remote Sensing of Environment200, 250-262. https://doi.org/10.1016/j.rse.2017.08.018

Kilometric and hectometric biophysical products are now widely available with almost complete and continuous coverage, but the associated spatial resolution limits the application over heterogeneous landscapes. The objective of this study is to combine unfrequent decametric spatial resolution products with frequent hectometric spatial resolution products to improve the temporal frequency and completeness of decametric observations. The study focuses on the fraction of photosynthetically active radiation absorbed by the green vegetation (FAPAR) because of its important role in canopy models and small dependency to scaling issues.

  • Velumani, K., Oude Elberink, S., Yang, M. Y., & Baret, F. (2017). Wheat Ear Detection in Plots by Segmenting Mobile Laser Scanner Data. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 149-156. https://doi.org/10.5194/isprs-annals-IV-2-W4-149-2017

The use of Light Detection and Ranging (LiDAR) to study agricultural crop traits is becoming popular. Wheat plant traits such as crop height, biomass fractions and plant population are of interest to agronomists and biologists for the assessment of a genotype's performance in the environment. Among these performance indicators, plant population in the field is still widely estimated through manual counting which is a tedious and labour intensive task. The goal of this study is to explore the suitability of LiDAR observations to automate the counting process by the individual detection of wheat ears in the agricultural field.

 

  • Weiss, M., & Baret, F. (2017). Using 3D Point Clouds Derived from UAV RGB Imagery to Describe Vineyard 3D Macro-Structure. Remote Sensing9(2), 111. http://dx.doi.org/10.3390/rs9020111

In the context of precision viticulture, remote sensing in the optical domain offers a potential way to map crop structure characteristics, such as vegetation cover fraction, row orientation or leaf area index, that are later used in decision support tools. A method based on the RGB color model imagery acquired with an unmanned aerial vehicle (UAV) is proposed to describe the vineyard 3D macro-structure.

 

  • Daumard, F., Goulas, Y., Baret, F., Moya, I., & Chelle, M. (2016, November). Simulating passive and active measurements of canopy fluorescence yield: a 3D approach. In FSPMA2016, International Conference on Functional-Structural Plant Growth Modeling, Simulation, Visualization and Applications, IEEE (p. np).

Current techniques for remote sensing of fluorescence aim at quantifying the laser-and sun-induced fluorescence (resp. LIF and SIF) within radiation going out vegetation. The CALSIF project aims at building a new airborne instrument for simultaneous measurement of LIF and SIF. To help the design of a new airborne instrument for simultaneous measurement of LIF and SIF as well as the interpretation of future measurements, we have developed a new 3D fluorescence model. It calculates the multispectral radiance (total and due to fluorescence) observed by a radiometer located above a 3D canopy lit either naturally or with a laser.

 

  • David Gouache, Katia Beauchêne, Agathe Mini, Antoine Fournier, Benoit de Solan, Fred Baret, Alexis Comar, "Applying remote sensing expertise to crop improvement: progress and challenges to scale up high throughput field phenotyping from research to industry," Proc. SPIE 9866, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping, 986604 (8 June 2016) http://dx.doi.org/10.1117/12.2229389

 We will present successive technological developments that have allowed the migration and application of remote sensing approaches at large into the field of crop genetics and physiology research, with a number of projects that have taken place in France. These projects have allowed us to develop combined sensor plus vector systems, from tractor mounted and UAV (unmanned aerial vehicle) mounted spectroradiometry to autonomous vehicle mounted spectroradiometry, RGB (red-green-blue) imagery and Lidar.

 

  • Verger, A., Vigneau, N., Chéron, C., Gilliot, J. M., Comar, A., & Baret, F. (2014). Green area index from an unmanned aerial system over wheat and rapeseed crops. Remote Sensing of Environment152, 654-664. https://doi.org/10.1016/j.rse.2014.06.006

Unmanned airborne systems (UAS) technology opens new horizons in precision agriculture for effective characterization of the variability in crop state at high spatial resolution and high revisit frequency. Green area index (GAI) is a key agronomic variable involved in many processes and used for decision making. This paper describes a physically based algorithm for estimating GAI from UAS acquisitions.

 

  •  Goulas, Y., Ounis, A., Daumard, F., Baret, F., Chelle, M., & Moya, I. (2014, April). Assessment of canopy fluorescence yield with airborne passive and active measurements: the CALSIF project. In 5th International Workshop on Remote Sensing of Vegetation Fluorescence (pp. 22-24).

Current techniques for large scale remote sensing of fluorescence aim at quantifying the sun-induced fluorescence (SIF) in absorption features present in the incoming radiation. However, these techniques allow measurement of the emitted fluorescence flux but do not provide a direct assessment of the fluorescence yield, which is the true variable linked to the physiological status of the plant. To overcome these limitations, the authors initiated, with the support from CNES and ANR, the CALSIF project whose main purpose is to build an airborne instrument for simultaneous measurement of laser induced fluorescence (LIF) and SIF.

 

  • Knyazikhin, Y., Schull, M. A., Stenberg, P., Mõttus, M., Rautiainen, M., Yang, Y., ... & Disney, M. I. (2013). Hyperspectral remote sensing of foliar nitrogen content. Proceedings of the National Academy of Sciences110(3), E185-E192. doi: 10.1073/pnas.1210196109 

 

A strong positive correlation between vegetation canopy bidirectional reflectance factor (BRF) in the near infrared (NIR) spectral region and foliar mass-based nitrogen concentration (%N) has been reported in some temperate and boreal forests. This relationship, if true, would indicate an additional role for nitrogen in the climate system via its influence on surface albedo and may offer a simple approach for monitoring foliar nitrogen using satellite data. We report, however, that the previously reported correlation is an artifact—it is a consequence of variations in canopy structure, rather than of %N.

 

  • Mõttus, M., Sulev, M., Baret, F., Lopez-Lozano, R., & Reinart, A. (2012). Photosynthetically Active Radiation: Measurement photosynthesis/photosynthetic (ally) active radiation (PAR) measurement and Modeling photosynthesis/photosynthetic (ally) active radiation (PAR) modeling. In Encyclopedia of Sustainability Science and Technology (pp. 7902-7932). Springer New York. https://doi.org/10.1007/978-1-4419-0851-3_451

 

  • Comar, A., Burger, P., de Solan, B., Baret, F., Daumard, F., & Hanocq, J. F. (2012). A semi-automatic system for high throughput phenotyping wheat cultivars in-field conditions: description and first results. Functional Plant Biology39(11), 914-924. https://doi.org/10.1071/FP12065

A semi-automatic system was developed to monitor micro-plots of wheat cultivars in field conditions for phenotyping. The system is based on a hyperspectral radiometer and 2 RGB cameras observing the canopy from ~1.5 m distance to the top of the canopy. Potentials of such semi-automatic measurement systems are discussed for in field phenotyping applications.

 

  • López-Lozano, R., Baret, F., de Cortázar Atauri, I. G., Lebon, E., & Tisseyre, B. (2011). 2D approximation of realistic 3D vineyard row canopy representation for light interception (fIPAR) and light intensity distribution on leaves (LIDIL). European journal of agronomy35(3), 171-183. https://doi.org/10.1016/j.eja.2011.06.005

A detailed dynamic 3D architecture model coupled with a soil water balance model is used to explore the pertinence of 2D approximations of the row structure and describe the light regime and canopy photosynthesis in vineyard. The fraction of intercepted light (fIPAR), the fraction of illuminated leaf area (fILA) and the distribution of light intensity on illuminated leaves (LIDIL) were calculated using Z-buffer and ray tracing techniques under both direct (black-sky) and diffuse (white-sky) conditions.

 

  • Baret, F., De Solan, B., Lopez-Lozano, R., Ma, K., & Weiss, M. (2010). GAI estimates of row crops from downward looking digital photos taken perpendicular to rows at 57.5 zenith angle: Theoretical considerations based on 3D architecture models and application to wheat crops. Agricultural and Forest Meteorology150(11), 1393-1401. https://doi.org/10.1016/j.agrformet.2010.04.011

This study describes a technique to estimate green area index (GAI) of row crops from gap fraction measurements at 57.5° perpendicular to the row using downward looking digital photos. This particular directional configuration makes the gap fraction independent from leaf angle distribution and minimizes leaf clumping when plants overlap within the row and when rows overlap from this particular direction which is the case for several crops including wheat, maize, sorghum, sunflower and soybean. This was demonstrated from generic row crop canopy architecture models.

 

  • Casa, R., Baret, F., Buis, S., Lopez-Lozano, R., Pascucci, S., Palombo, A., & Jones, H. G. (2010). Estimation of maize canopy properties from remote sensing by inversion of 1-D and 4-D models. Precision agriculture11(4), 319-334. http://dx.doi.org/10.1007/s11119-010-9162-9

The inversion of canopy reflectance models is widely used for the retrieval of vegetation properties from remote sensing. However the accuracy of the estimates depends on a range of factors, most notably the realism with which the canopy is represented by the models and the possibility of introducing a priori knowledge on canopy characteristics to constrain the inversion procedure. The objective of the present work was to compare the performances and operational limitations of two contrasting types of radiative transfer models: a classical one-dimensional canopy reflectance model, PROSPECT+SAIL (PROSAIL), and a three-dimensional dynamic (4-D) maize model. 

 

  • Dorigo, W., Richter, R., Baret, F., Bamler, R., & Wagner, W. (2009). Enhanced automated canopy characterization from hyperspectral data by a novel two step radiative transfer model inversion approach. Remote Sensing1(4), 1139-1170. http://dx.doi.org/10.3390/rs1041139

Automated, image based methods for the retrieval of vegetation biophysical and biochemical variables are often hampered by the lack of a priori knowledge about land cover and phenology, which makes the retrieval a highly underdetermined problem. This study addresses this problem by presenting a novel approach, called CRASh, for the concurrent retrieval of leaf area index, leaf chlorophyll content, leaf water content and leaf dry matter content from high resolution solar reflective earth observation data.

 

  • López-Lozano, R., Baret, F., de Cortázar-Atauri, I. G., Bertrand, N., & Casterad, M. A. (2009). Optimal geometric configuration and algorithms for LAI indirect estimates under row canopies: The case of vineyards. Agricultural and Forest Meteorology149(8), 1307-1316. https://doi.org/10.1016/j.agrformet.2009.03.001

Leaf Area Index (LAI) retrieval performances from gap fraction measurements are investigated over vertically trained vineyards. A 3D vineyard model was constructed to analyze the influence of canopy architecture characteristics and light direction on LAI estimation. Results show that for specific directions – close to zenith and parallel to the rows – gap fraction (Po) is mainly driven by vineyard architectural characteristics with small effect of LAI due to the clumped foliage distribution.