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The balance-sheet method has been widely used for decades as a reference method to calculate the amount of nitrogen (N) fertilizer to supply to winter wheat. With this method, the amount of N fertilizer is calculated at the end of the winter to support the N requirement for an estimated yield level. This method is now being questioned because it has become difficult to predict yields, given climate change and the increased presence of pests in agroecological cropping systems, resulting in greater uncertainty about the yield achieved compared to conventional systems of the past. Alternative methods, such as the innovative approach of nitrogen fertilization (APPI-N; Ravier et al., 2018), are proposed for dynamic fertilization management: the amount of nitrogen fertilizer is adjusted to the real nitrogen status of the plant using the N nutrition index (NNI). Lemaire et al. (1989) define the NNI as the ratio of the measured nitrogen concentration (%N) to the critical nitrogen concentration (%Nc; Justes et al., 1994); it can be indirectly determined by measuring the leaf chlorophyll content using hand-held nondestructive contact devices (i.e. N-Tester, SPAD). However, their use is time-consuming and not suitable for routine use by farmers or advisors. Different optical measurements by proxi- or remote sensing using leaf color charts or vegetation indices combining visible and infrared wavebands at leaf or canopy level seem promising for estimating the nitrogen status of winter wheat. In the literature, a dark green color index (DGCI), was specifically created for the purposes of leaf nitrogen concentration analysis using visible images (Fig.1). The range of DGCI values is between 0 (very yellow; low N rate) and 1 (dark green; high N rate). Indeed, nitrogen is directly related to leaf color because it is a key component of the chlorophyll molecule (Tracy et al., 1992). First studied on grass (Karcher and Richardson, 2003), it was then successfully tested on maize crops (Rorie et al., 2011), provided that the images were normalized to correct for differences in lighting conditions during and between experiments and differences between cameras. In order to better understand the relationships between DGCI measurement methodology and nitrogen fertilization, different experiments were conducted at a canopy level. The objectives of this work were to explore the potential of the DGCI in winter wheat crop at canopy level for estimating NNI deduced from conventional methods (N-Tester). The trial zone represented 24 microplots: half of them were seeded with a winter wheat (Triticum aestivum L.) cv. LG Absalon and the other half with a mixture of cultivars (LG Absalon, Lipari, Rubisko, Tenor). To create a nitrogen gradient, the plots were fertilized according to five modalities (with two replicates) corresponding to different nitrogen doses ranging from 0 to 300 kgN.ha-1, with a nitrogen index ranging from 0.4 (minimal N status) to 1.2 (supra optimal N nutrition). A series of non-destructive measurements was performed on this N gradient during two growth stages, 2 nodes and heading. A chlorophyll-meter (N-tester) and three different optical systems (a digital camera and two smartphones) were used to estimate indirectly the leaf chlorophyll concentration from NNI.To validate protocol for image acquisition, three factors were tested: 1) DGCI vs. relative DGCI to evaluate the impact of standardization of image data 2) different optical systems to evaluate their impact and that of the white balance (manual or automatic) on the quality of the results 3) a pure wheat cultivar vs. mixture of cultivar to evaluate the capacity of the DGCI face to different cultivars. Statistical analyses were conducted to investigate the correlations between the N-Tester measurements and those deduced from images (DGCI and relative DGCI) using R software and RStudio, an integrated development environment for R. First results demonstrated that the relative DGCI values were highly correlated with NNI measurements with a significant degree of association. Best results were observed considering only one cultivar (LG Absalon). The “manual” option of white balance improved the results. Furthermore, there seemed to be a varietal effect depending on whether a mixture or a single cultivar is studied. As the results were only obtained in one year and at one location, further measurements are needed to validate this new methodology, which is easy to use and can be carried by any type of mobile platform (smartphone, drone, robot, etc.). From the results, the DGCI method developed on a large scale could be used as a nitrogen nutrition indicator on arable crops for dynamic monitoring of nitrogen fertilization.

In conventional cropping systems, arable weed species have always been considered undesirable because their presence can significantly reduce the yield and quality of the crop. Among the weed community, segetal weeds particularly inhabit winter cereal fields and are generally described as rare species (Jauzein, 1997). Following the observed decline in biodiversity in the field, there is a renewed interest in conserving segetal weeds as they represent an invaluable plant heritage as wild relatives of cultivated plants (Fried, 2020). The origin of the regression of these particular annual species is strongly linked to intensive agricultural practices (herbicides, fertilization) but also probably to a poorer competitive ability with recent selected cereal crops unlike common species (Denelle et al., 2020). To study in detail the competitive ability of these two categories of weeds, a wide range of species (7 segetal and 16 common species) was studied in the presence of a spring barley crop (Hordeum vulgare L.). For that purpose, experiments were carried out using mesocosms (rectangular plastic pot) as experimental units. Each mesocosm was divided into three studied zones: one quarter was dedicated to the growth of barley alone (Zone1), another quarter was dedicated to a weed alone (Zone4) and the remaining half was a mixture of barley and weeds (Zone2&3). For each mesocosm, only one weed species was considered. In total, on the 23 mesocosms, different competitiveness traits were measured during the plant development cycle between March and June 2021: plant height, above-ground biomass and total plant biomass. The calculation of the Relative Competitive Performance index (RCP) with height as a competitive trait revealed different competitive effects caused by weeds and a ranking of the weeds according to their competitive power is proposed and compared to the literature. Under the conditions of our experiment, it does not seem that segetal species showed a lower ability to compete than common species

The counting cells (Malassez, Thoma ...) are designed to allow the enumeration of cells under a microscope and the determination of their concentration thanks to the calibrated volume of the grid appearing in the microscopic image. Manual counting has major disadvantages: subjectivity, non-repeatability ... There are commercial automatic counting solutions, the disadvantage of which is that a well-controlled environment is required which can’t be obtained in certain studies ( eg glycerol greatly affects the quality of the images ). The objective of the project is therefore twofold: an automated cell count and sufficiently robust to be feasible regardless of the acquisition conditions.In a first step, a method based on the Fourier transform has been developed to detect, characterize and erase the grid of the counting cell. The characteristics of the grid extracted by this method serve to determine an area of interest and its erasure makes it easier to detect the cells to count.To perform the count, the main problem is to obtain a cell detection method robust enough to adapt to the variable acquisition conditions. The methods based on gradient accumulations have been improved by the addition of structures allowing a finer detection of accumulation peaks. The proposed method allows accurate detection of cells and limits the appearance of false positives.The results obtained show that the combination of these two methods makes it possible to obtain a repeatable and representative count of a consensus of manual counts made by operators., Les cellules de comptage (Malassez, Thoma …) sont conçues pour permettre le dénombrement de cellules sous microscope et la détermination de leur concentration grâce au volume calibré de la grille apparaissant dans l’image microscopique. Le comptage manuel présente des inconvénients majeurs : subjectivité, non-répétabilité… Il existe des solutions commerciales de comptage automatique dont l’inconvénient est de nécessiter un environnement bien contrôlé qu’il n’est pas possible d’obtenir dans le cadre de certaines études (ex. : le glycérol influe grandement sur la qualité des images). L’objectif du projet est donc double : un comptage des cellules automatisé et suffisamment robuste pour être réalisable, quelles que soient les conditions d’acquisition.Dans un premier temps, une méthode basée sur la transformée de Fourier a été développée pour détecter, caractériser et effacer la grille de la cellule de comptage. Les caractéristiques de la grille extraites par cette méthode servent à déterminer une zone d’intérêt et son effacement permet de faciliter la détection des cellules à compter.Pour réaliser le comptage, la problématique principale est d’obtenir une méthode de détection des cellules suffisamment robuste pour s’adapter aux conditions d’acquisition variables. Les méthodes basées sur les accumulations de gradients ont été améliorées par l’adjonction de structures permettant une détection plus fine des pics d’accumulation. La méthode proposée permet une détection précise des cellules et limite l’apparition de faux positifs.Les résultats obtenus montrent que la combinaison de ces 2 méthodes permet d’obtenir un comptage répétable et représentatif d’un consensus des comptages manuels réalisés par des opérateurs.