FFAST aims at describing wheat genotypes functioning through an innovative model-assisted phenotyping strategy. Currently, studies on field phenotyping are mostly focused on exploiting directly structural traits observations (e.g. leaf area, height) to establish statistical models with genetic characteristics. However, structural traits are highly determined by the environment, and such empirical models are insufficient to describe genotypes functioning. FFAST proposes an alternative approach using functional plant models (FPM, also known as crop process-based models) to describe the eco-physiological mechanisms that produce a differentiated response of the genotype to the environment (GxE). This model-assisted strategy consists in assimilating large observational datasets of multiple structural traits over different growing environments to retrieve, for each genotype, a set of varietal parameters of a FPM. These varietal parameters describe the genotype functioning and constitute functional traits, closely linked to its genetic characteristics. The model-assisted phenotyping method will be evaluated in a panel of ten bread wheat genotypes that will be monitored on phenotyping experiments and by satellite. The phenotyping experiments will be conducted in the Toulouse, Clermont-Ferrand and Montpellier sites –part of the PHENOME-EMPHASIS phenotyping infrastructure– during three years. That will permit to acquire high-throughput observations of multiple structural traits (leaf area, canopy height, heading date, ears density…) in different environments. Nevertheless, as a large environmental variability is essential to retrieve accurately functional traits, FFAST will investigate the use of high-resolution satellite platforms to provide additional cost-efficient observations of structural traits for specific genotypes over contrasted environments. Three genotypes of the panel will be monitored by satellite on 40 distant commercial fields over a climatic gradient in eastern France. Images from Sentinel 2 and PlanetScope satellite constellations will be used to retrieve frequent observations of some essential traits like the leaf green area index (GAI) and the fraction of absorbed photosynthetically active radiation (fAPAR). The estimation of functional traits from the observations will rely on a data assimilation framework based on the Sirius Quality FPM, specifically developed for wheat, which will be linked to the architectural model Adel Wheat. This will permit to improve the description of structure-driven processes such as light interception/absorption or evapotranspiration. Bayesian Monte Carlo methods will be used to retrieve varietal parameters of Sirius Quality from the structural traits observations for each genotype. The resulting posterior distribution of varietal parameters for all the genotypes will be analysed to identify those parameters –or groups of parameters characterizing the same mechanism– presenting statistically different posterior distributions among genotypes. Those parameters will constitute functional traits. The approach proposed by FFAST will be validated evaluating the reliability of the functional traits identified to predict the genotype performance in different environments from those used during the assimilation. This will permit to evaluate as well the role of remote sensing observations over different environments in the FFAST approach, compared to expensive multi-site phenotyping experiments. The project results will be disseminated through scientific papers in different domains: phenomics, eco- physiology, crop modelling and remote sensing. The observational datasets collected for the 10 genotypes will be also made public through a data paper. Moreover, the development of a methodology to produce multi-constellation GAI and fAPAR observations suitable for plant phenotyping will permit HIPHEN –enterprise partner in FFAST– to open new commercial services.

Vegetation plays a fundamental role in the functioning of land surfaces. A better understanding of the interactions between vegetation and atmosphere that would allow predicting the response of vegetation to future climate change is mandatory to design mitigation strategies. In this framework, remote sensing of vegetation mainly focused on the analysis of reflected sunlight in the optical domain to derive canopy biophysical variables, such as leaf area index, the fraction of reflected (albedo) or absorbed (fAPAR) radiation, or the content in chlorophyll pigments. Chlorophyll fluorescence is considered as a complementary observation compared to others based on reflectance. It is very promising since it is widely used to characterize the functioning of photosynthesis, which is a key parameter of the carbon cycle. Fluorescence emission occurs by a mechanism reverse to the absorption that directly competes with the photochemical conversion. This is the reason why fluorescence yield is closely linked to photosynthetic efficiency. Fluorescence yield variations can be directly monitored by active methods, because illumination conditions are well controlled. However, remote sensing of fluorescence with active techniques has been limited by the power of available excitation sources. During the last decade, a passive technique for measuring the sun-induced fluorescence (SIF) has been developed, and successfully applied for quantifying the fluorescence from space in the GOSAT mission. This technique is based on the analysis of the spectral absorption bands in the red and far red. Recently, ESA has selected the FLEX mission dedicated to fluorescence remote sensing. However, if the feasibility of measuring fluorescence from space has now been demonstrated, the interpretation of canopy fluorescence in terms of eco-physiological status still remains unclear. The purpose of this proposal is to develop the experimental and theoretical tools that will remove this bottleneck. We propose to develop a new instrument for measuring simultaneously the sun-induced and the laser-induced fluorescence (LIF) on the same target. LIF will enable the assessment of changes in the fluorescence yield, which is the true variable directly linked to photosynthesis efficiency, thus filling the gap between SIF and physiological status. In addition, the instrument will also measure: (i) vegetation reflectance spectrum to derive leaf area index, fAPAR or chlorophyll content, and (ii) the canopy vertical structure by monitoring the backscattered laser pulse. In parallel, we propose to develop a canopy fluorescence model to interpret sun-induced and laser-induced fluorescence measurements. Experimental studies will be carried on crops with different carbon metabolism (C3, C4). The obtained relationship between canopy fluorescence and photosynthesis will be tested against natural vegetation during airborne experiments. At the end of the project, the tools allowing to accurately measure and simulate canopy fluorescence signal will be made available to the scientific community. The outcomes of the project will from a strong scientific basis for the interpretation of fluorescence from space with data from FLEX, GOSAT or OCO-2.

WEEDELEC 2017 We propose in this project an alternative solution to global chemical weeding, which combines aerial means for weed detection coupled with a robotized ground weeding system based on high voltage electrical energy. The project will rely on commercial solutions concerning the aerial and ground vehicles (respectively UAV and robot). It will more particularly focus on major technical and scientific issues for the development of a future integrated weeding solution, i.e.: - weed detection and identification, using hyperspectral imagery and deep learning techniques - weed behavior when they are exposed to electrical stress, especially by investigating the relationship between the kind of electrical shock to be applied and the weed electrical impedance and phenology. Questions related to aerial and robot-embedded weed detection systems will also be addressed, as well as possible environmental and safety effects of electrical shock usage on weeds, in order to design a safe integrated weeding strategy. This project draws on previous results obtained in the FP4 European project Patchwork (electrical weeding 1995), in the FP7 European project RHEA (Integrated weeding solution, 2012) and on the Plant@Net project, devoted to automatic plant identification by deep learning. It also relies on the expertise of plant phenology scientists and weed scientists. The WeedElec project will be an opportunity to enrich Plant@net image databases, to produce a database of electrical signatures of main weed species, to develop and test new robust algorithms for weed detection and identification, and to validate an innovative weeding solution with no chemicals. The experimentations will be led in field crop and market gardening plots, in order to cover variate crop and weed typologies

AZODURE aims to develop Azospirillum inoculation technology for cereal seeds so that it can be implemented in the next decade as an alternative agricultural practice to reduce fertilization needs and plant sensitivity to erratic climatic fluctuations, and satisfy social aspiration for an environmentally friendly agriculture that maximizes field eco-efficiency. Azospirillum is a naturally-occurring associative symbiotic bacterial genus that enhances cereal roots development and performance, therefore enhancing crop performance. AZODURE is a multidisciplinary and industrial research project that innovates on its three objectives : 1/ industrial agroengineering innovation development with concern for the seed inoculation technology and the legal framework; 2/ gain of knowledge on ecosystemic medium-term (4 years) benefits (for plants, soil natural bacterial communities and soil water retention capacity) as well as crop productivity and economic gain; 3/ interaction with the socio-economic, political and higher education actors for outreach and dissemination of technology implementation. Partners and actors will work jointly on 9 closely-linked tasks. The Isère/Porte-des-Alpes (IPA) territory will serve as framework to develop these tasks mobilizing a range of scientific disciplines (microbial ecology, plant ecophysiology, agronomy and soil science, economy, law) along with a range of stakeholders (a private company, farmer organizations, policy makers, education actors).