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This paper presents the way the digital transformation of the agricultural sector is implemented in Europe and in France. It describes the main European and national strategies, the structure of research and innovation initiatives, and the investment in capacity building to foster innovation, adoption and use. More specifically, the French research and innovation ecosystem on digital agriculture is described. The actors involved come from different organisations, such as research and higher educational institutes, government agencies, agricultural technology (AgTech) companies, farmer unions etc., and work together by means of associations (e.g. Robagri), networks (e.g. RMT Naexus, DigiFermes, Fermes Leader), or living labs (e.g. Occitanum) on both digital technology assessment and co-design. Additionally, support is devoted to capacity building (e.g. Le Mas numérique , Mobilab) and a better understanding of the drivers of adoption and use of digital technologies (e.g. FrOCDA). Among these various organisations, #DigitAg, the Digital Agriculture Convergence Lab, has been created to foster interdisciplinary research on digital agriculture. All these initiatives aim to use digital technologies to support the European Green Deal, Farm-to-Fork and Biodiversity strategies as well as the French orientation towards more agroecological practices for safer and more sustainable food systems. Even though this organisational ecosystem is developing fast, the objective of encouraging the coevolution of both digital and green transformations is not without challenges that still need to be overcome, either through new research, innovations, initiatives or collaborations between the actors involved.

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By use of time-resolved spectroscopy it is possible to separate light scattering effects from chemical absorption effects in samples. In the study of propagation of short light pulses in turbid samples the reduced scattering coefficient and the absorption coefficient are usually obtained by fitting diffusion or Monte Carlo models to the measured data by use of numerical optimization techniques. In this study we propose a prediction model obtained with a semiparametric modeling technique: the least-squares support vector machines. The main advantage of this technique is that it uses theoretical time dispersion curves during the calibration step. Predictions can then be performed by use of data measured on different kinds of sample, such as apples.

The sugar content of Golden Delicious apples is predicted using near infrared (NIR) spectrometry. The study focuses on the metrological characteristics of the sugar content measurement and external parameters involved in the lack of robustness of the NIR-based model. The external parameters were fruit temperature, spectrometer temperature and ambient light. The first two factors influenced the prediction accuracy: (i) a fruit temperature variation altered the prediction, the relationship seems to be described by a non-linear model within the considered temperature range, (ii) a variation of the spectrometer temperature also altered the prediction, the relationship is described by a linear function for a temperature between 4 and 30°C. Ambient light did not show to have any influence on the NIR-based model. The analysis of the metrological parameters showed a satisfactory repeatibility in sugar prediction with a low error, 0.073°Brix. The model reproducibility was good regarding bias-corrected standard error of prediction (SEPc) without significant differences between experiments, on the other hand a bias remained even if the previous parameters were maintained constant. These results will be taken into account in future measurements, in order to improve the robustness of the NIR-based model developed for apples.

The sugar content of Golden Delicious apples is predicted using near infrared (NIR) spectrometry. The study focuses on the metrological characteristics of the sugar content measurement and external parameters involved in the lack of robustness of the NIR-based model. The external parameters were fruit temperature, spectrometer temperature and ambient light. The first two factors influenced the prediction accuracy: (i) a fruit temperature variation altered the prediction, the relationship seems to be described by a non-linear model within the considered temperature range, (ii) a variation of the spectrometer temperature also altered the prediction, the relationship is described by a linear function for a temperature between 4 and 30°C. Ambient light did not show to have any influence on the NIR-based model. The analysis of the metrological parameters showed a satisfactory repeatibility in sugar prediction with a low error, 0.073°Brix. The model reproducibility was good regarding bias-corrected standard error of prediction (SEPc) without significant differences between experiments, on the other hand a bias remained even if the previous parameters were maintained constant. These results will be taken into account in future measurements, in order to improve the robustness of the NIR-based model developed for apples.

In fermentation processes, kinetic curves are generally aimed at control purposes. However, these curves could also contain information about inherent features of the product (such as origin, quality, etc.). This article presents several pattern analysis techniques used to classify fermentation curves. An application to alcoholic fermentation is presented as an illustration: it aims at retrieving the origin of a must from its fermentation curve. The fermentation kinetics of five vineyard musts, harvested over 9 years on the same parcels, were recorded. From these curves two sets of variables were generated: The first (p1) gathers all the kinetic curve points. The second (p2) contains a restrained number of variables, generated by the expert knowledge of the enologist. The set p2 was processed by two very different techniques: a linear one (factorial discriminant analysis) and a nonlinear one (artificial neural networks). The set p1 was processed by a new chemometric technique, the discriminant partial least-squares regression. For all the sets and the techniques used the selection of variables was studied. The interest in the latter is largely demonstrated both by theoretical and practical discussions. The discrimination results (up to 94% of good predictions) enhance the interest of the on-line measurements and their use in such pattern analysis tools. © 2002 Wiley Periodicals, Inc. Biotechnol Bioeng 79: 804–815, 2002.

Earlier experiments have confirmed the suitability of Fourier transform infrared attenuated total reflectance (FT-IR/ATR) spectroscopy for monitoring sugar production from starch hydrolysis. However, in order to confirm the suitability of this technique for an industrial use, the influence of external factors (proteins, salts, temperature) on the prediction, as well as the influence of wavelength parameters, must be evaluated. This paper shows that proteins and salts, which occur in low concentrations in sugar solution before purification, have virtually no influence on the detection of sugar. However, sample temperature, already a very important factor in the industrial hydrolysis of starch, does have an influence upon spectral measurements. This influence would therefore affect the prediction ability of a model made at a single temperature. A proven solution to the influence of temperature is to establish a model which takes into account the range of temperatures that occur during the hydrolysis process. Variations in wavelength parameters also have been shown to significantly affect prediction ability.

This paper discusses the use of Fourier transform infrared (FT-IR) spectroscopy coupled with an attenuated total reflectance (ATR) accessory as applied to the quantification of individual sugar concentrations (glucose, maltose, maltotriose, and maltodextrines) in real mixtures extracted during starch hydrolysis. Solutions studied contained dry matter ranging between 250 and 300 g/kg. Glucose and maltose were detected with the required precision, but not maltotriose or maltodextrines. The measurements are fairly repeatible, and predictions are reproducible.

This paper discusses the use of Fourier Transform Infrared (FT-IR) spectroscopy coupled with an Attenuated Total Reflectance (ATR) accessory as applied to the quantification of individual sugar concentrations in mixtures extracted from starch hydrolysis (250-300 g/kg). Glucose and maltose are detected with the required precision, but not maltotriose or maltodextrines. The influence of external factors (proteins, salts, temperature) on the prediction is also evaluated. Proteins and salts, which occur in low concentrations in sugar solution have virtually no influence on the detection of sugar. Sample temperature does have an influence upon spectral measurements. In order not to affect the prediction ability, this influence is removed by the introduction of temperature variation in the model.

The agro-ecological transition is an ambitious challenge. It can be met by implementing the fundamentals of agroecology (use of biodiversity, integration of agriculture in landscapes, closure of flow loops) in the context of a broad and renewed offer of technologies: agro-equipment, biotechnology, digital technologies… This article explores the role that agro-equipment and digital services can play in this transition. These technologies contribute through various levers to the agro-ecological transition: by improving farming efficiency (more service rendered for the same environmental impact), by precision farming (adaptation of the operations to the needs of the plant or the animal based on a monitoring–diagnosis–recommendation cycle) and by the development of specialized machinery helping the farmer to achieve “flow loop-closing” (at the plot level, by maintaining the soil quality, or at the farm level, with the recycling of organic effluents) or to take advantage of biodiversity (e.g., with agro-equipment adapted to mixed crops). The technological bricks that are requested and for which advances are expected are: sensors (to measure plant or animal needs) and associated digital technologies (information transfer, data processing), precision technologies for input application, robotics, specialized machines to manage soil cover and weeds, or for agroforestry. The brakes and engines for innovation in agro-equipment are studied. The brakes are the generally small structure of the farm manufacturing companies, the deficit of the demand from farmers and the complexity − either real or perceived − of these equipments. To encourage innovation, several levers are to be used: involving users in the design of agro-equipments, creating financial incentives for innovative equipment purchase, and training end-users, prescribers and dealers to the high potential of these new technologies. In conclusion, putting agro-equipment and digital technology at the service of agroecology is not a straightforward route, but it is above all a real opportunity to produce better, technically and organizationally, with the emergence of new solidarities (sharing of data and knowledge). This is why it is absurd, as it is sometimes read, to oppose agroecology and technology. Agroecology is a set of practices to be built, while agro-equipment and digital technologies are a set of resources to be mobilized, with others, to achieve the objectives of sustainable agricultural production.