欧亚国际

随着遥感、机器人技术、计算机视觉和人工智能的发展，植物表型组学研究已经步入了快速成长阶段。出现了各类室内和室外表型技术载体平台，如Hiphen公司的手持、车载、航空机搭载、田间实时监控、大型室内外自动化平台等（搭载Airphen多光谱相机。室内、外植物研究中核心问题是对表型研究中产生的巨量图像和传感器数据进行量化分析，把大数据转化为有实际意义的性状信息和生物学知识，对后期表型数据解析尤其重要。

剥离自法国农业科学院以及Arvalis植物研究院的Hiphen公司代表了近地遥感以及植物表型研究领域的较高水准，其系统已经为众多先进科学研究机构以及育种公司等采用，hiphen公司开发的系列室外表型成像系统如下所示，除了自身的生产的多光谱相机，还可以集成高光谱、红外、叶绿素荧光等各个模块：F-cover：覆盖率，GF：植被覆盖度，GAI：绿地指数，ALA：平均叶角，FiPAR：冠层光能载获率，Fapar：光合有效辐射，植被指数：NDVI、MTCI、MCARI2…，Chl：叶绿素含量，综合变曲线参数（AUC）。 

近年来，基于Hiphen设备和算法，发表了数十篇文章，其中有Remote Sensing，Plant Phenomics等期刊。 

Impact of the reproductive organs on crop BRDF as observed from a UAV

Jun 2021

Several crops bear reproductive organs (RO) at the top of the canopy after the flowering stage, such as ears for wheat, tassels for maize, and heads for sunflowers. RO present specific architecture and optical properties as compared to leaves and stems, which may impact canopy reflectance. This study aims to understand and quantify the influence of...

Estimates of maize plant density from UAV RGB images using Faster-RCNN detection model: impact of the spatial resolution 

Early-stage plant density is an essential trait that determines the fate of a genotype under given environmental conditions and management practices. The use of RGB images taken from UAVs may replace traditional visual counting in fields with improved throughput, accuracy and access to plant localization. However, high-resolution (HR) images are re...

Plant detection and counting from high-resolution RGB images acquired from UAVs: comparison between deep-learning and handcrafted methods with application to maize, sugar beet, and sunflower crops

Plants density is a key information on crop growth. Usually done manually, this task can beneficiate from advances in image analysis technics. Automated detection of individual plants in images is a key step to estimate this density. To develop and evalsuate dedicated processing technics, high resolution RGB images were acquired from UAVs during sev...

FASPECT: A model of leaf optical properties accounting for the differences between upper and lower faces

Many plant species have distinct optical properties between upper and lower leaf faces. These differences between faces are mainly attributed to the non-homogeneous distribution of absorbing and scattering materials within the leaf depth as well as particular surface features of both epidermises. We proposed the FASPECT model which is an evolution...

Cercospora Leaf Spot on Sugar Beet: Comparison of UGV and UAV Phenotyping Systems

Selection of sugar beet (Beta vulgaris L.) cultivars that are resistant to Cercospora Leaf Spot (CLS) disease is critical to increase yield. Such selection requires an automatic, fast, and objective method to assess CLS severity on thousands of cultivars in the field. For this purpose, we compare the use of submillimeter scale RGB imagery acquired...

Supplementary Material_Blancon et al. 2019 Phenotyping Maize GLAI Dynamics Frontiers in Plant Science.pdf

An automatic method based on daily in situ images and deep learning to date wheat heading stage

Accurate and timely observations of wheat phenology and, particularly, of heading date are instrumental for many scientific and technical domains such as wheat ecophysiology, crop breeding, crop management or precision agriculture. Visual annotation of the heading date in situ is a labour-intensive task that may become prohibitive in scientific and...

A High-Throughput Model-Assisted Method for Phenotyping Maize Green Leaf Area Index Dynamics Using Unmanned Aerial Vehicle Imagery

The dynamics of the Green Leaf Area Index (GLAI) is of great interest for numerous applications such as yield prediction and plant breeding. We present a high-throughput model-assisted method for characterizing GLAI dynamics in maize (Zea mays subsp. mays) using multispectral imagery acquired from an Unmanned Aerial Vehicle (UAV). Two trials were c...

High-Throughput Measurements of Stem Characteristics to Estimate Ear Density and Above-Ground Biomass 

Total above-ground biomass at harvest and ear density are two important traits that characterize wheat genotypes. Two experiments were carried out in two different sites where several genotypes were grown under contrasted irrigation and nitrogen treatments. A high spatial resolution RGB camera was used to capture the residual stems standing straigh...

UAV very high-resolution observations to quantify vegetation state, phenology and health

Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet crops 

nned aerial vehicles (UAV) has opened a new horizon in vegetation remote sensing, especially for agricultural applications. However, the benefits of UAV centimeter-scale imagery are still unclear compared to coarser resolution data acquired from satellites or aircrafts. This study aims (i) to propose novel methods for re...

Leaf-rolling in maize crops: From leaf scoring to canopy-level measurements for phenotyping 

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...

Estimation of leaf traits from reflectance measurements: Comparison between methods based on vegetation indices and several versions of the PROSPECT model 

Background: Leaf biochemical composition corresponds to traits related to the plant state and its functioning. This study puts the emphasis on the main leaf absorbers: chlorophyll a and b ([Formula: see text]), carotenoids ([Formula: see text]), water ([Formula: see text]) and dry mater ([Formula: see text]) contents. Two main approaches were used...

High-Throughput Phenotyping of Plant Height: Comparing Unmanned Aerial Vehicles and Ground LiDAR Estimates

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. Frequent LiDAR measurements were performed along the growth cycle using a phénomobiles unmann...

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

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 a...

Estimating leaf chlorophyll content in sugar beet canopies using millimeter- to centimeter-scale reflectance imagery

Accurate estimation of leafchlorophyll content (Cab) from remote sensing is of tremendous significance to mon- itor the physiological status ofvegetation 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 t...

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 evalsuate a method for estimating wheat plant density at the emergence stage based on high resolution imagery taken fr...

A method to estimate plant density and plant spacing heterogeneity: Application to wheat crops

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. ResultsThree experiments were conducted in 20...

Estimation of leaf chlorophyll content in sugar beet canopies using mm- to cm-scale reflectance imagery

For further information, please see the following journal paper: S. Jay, N. Gorretta, J. Morel, F. Maupas, R. Bendoula, G. Rabatel, D. Dutartre, A. Comar, F. Baret: Estimating leaf chlorophyll content in sugar beet canopies using millimeter- to centimeter-scale reflectance imagery. Remote Sensing of Environment, 2017; 198:173-186.

Remote sensing for crop improvement: From research to industry

Plant breeding is costly and time -consuming, and any technology that can help predict the performance of each genotype while reducing the cost and amount of testing greatly improves the overall rate of genetic gain. In response to this problem, we developed a prototype remote -sensing -based system to characterize the response of wheat varietie...

Water stress field phenotyping and PHENOmobiles-LV monitoring of wheat

The PHENOmobiles -LV is a fully automated robot designed for high -precision, high -throughput field phenotyping. It is equipped with several sensors including RGB cameras, spectroradiometers working in the visible and near infrared, and LIDARs. All these measurements are performed from nadir and inclined directions to gather complementary informati...

Applying remote sensing expertise to crop improvement: progress and challenges to scale up high throughput field phenotyping from research to industry

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 approac...

High throughput phenotyping for complex traits : case study for nitrogen response in wheat based on the PhénoBlé project. 

Crop response to abiotic stress is a complex trait, muddled by genotype by environment interactions, and multiple underlying traits. This is typically the case for response to nitrogen in wheat. High throughput phenotyping provides access to intermediate level traits that can help in understanding, screening, and ultimately ameliorating nitrogen re...

From PhénoBlé to AirPhen : field phenotyping technologies take off

Field phenotyping technologies are regularly cited as one of the new frontiers to accelerate genetic progress in plant breeding. Remote sensing specialists regrouped in Avignon, France have led successive research and development projects to create a number of new technological tools that allow canopy traits to be screened on large panels of genoty...

High-throughput field phenotyping: bridging scales from gene to canopy for trait discovery

Recent improvements in phenomics have opened up vast opportunities to understand and predict dynamic phenotypic responses to genotypic variation. To date however, a great majority of phenomics studies are conducted in controlled condition facilities, such as growth chambers and greenhouses, in which plants function as individuals. In crop species h...

PHENOmobiles-V1 A fully automated high throughput phenotyping system

Green area index from an unmanned aerial system over wheat and rapeseed crops

ACT: A leaf BRDF model taking into account the azimuthal anisotropy of monocotyledonous leaf surface

Leaf reflectance of monocotyledons generally displays a strong azimuthal anisotropy due to the longitudinal orientation of the veins. The Cook and Torrance (CT) bidirectional reflectance distribution function model was adapted to account for this distinctive feature. The resulting ACT (Anisotropic Cook and Torrance) model is based on the decomposit...

ACT: A leaf BRDF model taking into account the azimuthal anisotropy of monocotyledonous leaf surface

Keywords: Sorghum Wheat Leaf Surface roughness BRDF BRF Reflectance Conoscope Azimuthal anisotropy Optical properties Goniometer Physical model Refractive index Leaf reflectance of monocotyledons generally displays a strong azimuthal anisotropy due to the longitudinal orientation of the veins. The Cook and Torrance (CT) bidirectional reflectance di...

ACOMAR RSE Wheat leaf BRDF

A semi-automatic system for high throughput phenotyping wheat cultivars in-field conditions: Description and first results 

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 similar to 1.5 m distance to the top of the canopy. The system allows measurement from both nadir and oblique views inclined at 57.5 degree...

Wheat leaf bidirectional reflectance measurements: Description and quantification of the volume, specular and hot-spot scattering features 

This study focuses on the directionality of wheat leaf reflectance as a function of leaf surface characteristics. Wheat leaf BRF measurements were completed under 45° zenith illumination angle in three visible broad spectral bands with a conoscope that provides very high angular resolution data over a large portion of the whole hemisphere, includin...

Green area index from an unmanned aerial system over wheat and rapeseed crops

Keywords: Green area index Radiative transfer inversion Lookup tables Unmanned airborne systems Precision agriculture 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. Gree...