Every year, #DigitAg grants Master 2 Internships for French and foreign students
In 2022, subjects from different disciplines are suggested in Human&Economic Sciences, Environment&Life sciences, as well as in Engineering and Mathematics.
- To apply please send your curriculum and application letter to the relevant contact from the list below
- Allowance: #DigitAg gives internships grants to the research units: the amount of your allowance is to be asked to the contact person
|Improvement of an event detection tool (reproductive, sanitary, malfunction) in a herd of dairy cows and several groups of sows
Keywords : precision farming, event detection, water consumption, multi-species
Precision farming tools combined with the breeder’s observations, allow individual and automated tracking of dairy cows and sows contributing to an earlier detection of events (calving, health problems, malfunction). From water consumption data, an analysis method made it possible to detect disturbances related to health, reproduction or technical dysfunction events. This method is more than 95% specific for cows and sows, however its sensitivity is at best around 70% for cows and remains lower for sows (<50%).
|Supporting decision-making in precision spraying with a study on the relationship between statistical distribution and 3D spatial covering of plant protection products in plants
Filled internshipKeywords : geostatistics, 3D spatial data, grapevine, spraying, LiDAR, precision agriculture
The agro-environmental effects that result from spraying plant protection products (PPP) is related to (a) the PPP quantities intercepted by the canopy, (b) the 3D covering of leaves within the canopy, and (c) the losses on the ground or to the air (drift). The sampling methodologies that we have developed to study the quantities that are intercepted in the vine and their 3D repartition involve artificial collectors that are set according to a precise tri-dimensional scheme. Spraying a tracer that is intercepted by these collectors allows for the assessment of deposits in the canopy. The quantitative analysis of deposits data has been performed until now using statistical distributions and without making explicit in the results the spatial information that is attached to the known 3D setting of collectors. Our work has brought forward the importance of the geometrical structure of the canopy and its density as measured by a LiDAR to explain the statistical distribution of deposits obtained on real vine. Research that extend the results is today necessary to lead to a precise and sound agronomical decision that meets new stakes and requirements attached to new PPP (biocontrol notably).
|Development of a rape winter stem weevil flights predictive tool, based on the « Vigiculture » database
Keywords : datascience, machine learning, interpretability, digital agriculture, DST, rapeseed, rape winter seed weevil
Ceuthorynchus picitarsis larvae can destroy the terminal bud of rapeseed. The management relies on the use of insecticides which target the adults before the beginning of egg laying. This insect can only be observed in the field thanks to the use of yellow traps which attract them to a certain extent. The optimal date of treatments is based on the precise detection of the arrival of the insects in the fields. The ability to predict flights is therefore essential to optimise the use of insecticides in terms of positioning and avoidance of useless treatments. The « Vigiculture » database agregate field observations since 2008. It is used to edit the « Bulletin de Santé du Végétal » (a weekly report about crop sanitary state) that helps farmers in their decisions. Nonetheless, those data are scarcely used to build predictive tools. The database contains more than 52000 observations over 10000 fields between 2008 and 2020 regarding C. picitarsis.
|UAV as an intermediate step for the mapping of vegetation at National level
Keywords : UAV, biomass, mapping , pastoral livestock
Savannas are ecosystems with a strong spatial heterogeneity for both herbaceous and woody vegetation. The quantification of this vegetation (biomass) is a key point in these arid zones for pastoral livestock for example. Field measurements allow to measure biomass over small areas. But how do you get this information over a larger area? Indeed, the pastoral with the high mobility of the animal used the vegetation on large area. Studies show the possibility to establish relationships between field data and open access of medium-resolution satellite images covering the whole country.
– produce vegetation maps considering the heterogeneity of the vegetation
– then make a link between these maps and images with lower spatial resolutions.
|High-throughput phenotyping of functional traits in grapevine to characterize the genetic variability of its responses to abiotic stresses
Keywords : Phenotyping, high-throughput, near infrared spectrometry, wineyard, drought, photosynthesis, transpiration
Climate change is likely to bring vineyards to levels of water stress that are critical for the production and quality of wines. New varieties are therefore being sought in order to save water while maintaining their photosynthesis, which is required for yield maintenance. Phenotyping these traits in the vineyard on large numbers of grapevines is thus a major prerequisite in order to i) integrate them into breeding programs, and ii) make decision criteria for these traits available to winegrowers. However, the perennial nature of the vine, the cost and the low throughput of conventional measurement methods (photosynthesis/transpiration) clearly limit their deployment at high throughput in the vineyard. The project aims to develop and test the application of new methods of high-throughput phenotyping of functional traits on large populations of interest in order to assess the genetic variability for these traits. Particular interest will be given to the use of near infrared spectrometry (NIRS) and chlorophyll fluorescence as proxies of photosynthetic and hydraulic functions. The work will be undertaken on 2 populations: a half-diallel mating design, and a diversity panel of 279 varieties. The work is divided into two sub-objectives: (a) a calibration phase on a subset of genotypes, on which fine physiological measurements will be coupled to rapid measurements, to establish prediction models of physiological traits (photosynthesis, leaf metabolites, stomatal conductance, etc.) from high-throughput measurements. (b) the deployment of high throughput measurements on whole populations, the prediction of traits of interest by the models established in (a), and the analysis of the variability and genetic determinants of these traits by association genetics.
|Assessment of the potential of the Pl@ntNet platform for the identification of pasture species in support of pastoralist management strategies
Keywords : pl@ntnet, Pastures, botanical identification, Artificial intelligence
The participatory science platform Pl@ntNet offers various web services to help identify plant species from the automated visual analysis of plant photos. The validated data it generates is used for training automated visual classification models, allowing species identification from photos of leaves, flowers, fruits or stems.
|Uses of WhatsApp groups for the exchange of experiences among farmers. Case study in Benin.
Keywords : Social network, digital humanities, smartphones, WhatsApp, agriculture, Benin
Digital technology in agriculture opens the door to many innovations, including an increased capacity to generate and exchange knowledge among peers. Observations made in West Africa lead to two observations. (1) Many of the tools developed are not used (no clear business model, little consideration of the needs and capacities of users). (2) Despite the spectrum of innovations associated with the imaginary of digital agriculture, social networking applications such as WhatsApp (WA) via cell phone are by far the most used. Farmers easily create groups to interact with several dozen or hundreds of people. These groups are formed around various topics (purchases/sales, crop…) or territories and allow the exchange of texts, audios, photos, videos at low cost. However, WA has many limitations (parallel conversations, data storage, complicated information search, difficulty for long exchanges).
– What are the uses of social networks like WA by farmers?
– How does this arena compete with and/or complement other sharing and socialization spaces?
Anchored between management and sociology, the methodology will be mixed: observation and characterization of interactions, virtual or direct exchanges with group members, and quantitative analyses of groups.
|Digital tools in the design of agroforestry systems. State of the art of practices and needs
Keywords : codesign workshop, agroforestry
The design of agroforestry systems, and in particular the design of agroforestry systems, is a complex issue involving multiple stakeholders with different points of view and both implicit and explicit knowledge and experience. Moreover, the effects of the decisions made will only be perceptible in the long term. The research work of the last decade shows that co-design workshops are a relevant way to reach operational solutions. In these workshops, the use of digital technology remains limited but is a promising approach. The development of generalist tools is hampered by the variety of workshop practices and the limited availability of literature on the subject.
– What visualizations of ecosystem services are most needed?
– What gaps in current tools/methods are most felt by stakeholders?
– Is it necessary, and if so, how should the spatial and temporal dimension of systems be represented?
|Digitalization in livestock farming systems: Towards work organizations for the agroecological transition
filled internshipKeywords : Livestock farming; agroecology; digitalization; work; working conditions; work organization; advisory
The recent movement of digitalization in livestock farming requires to update the understanding of the activity conditions of the users. For instance, digital tools are proposed to livestock farmers for an agroecological transition purpose (by example, software for the grazing management) or for improving their work (milking robot, …).
|Navigation in implication rules extracted from agroecological knowledge in animal and plant health for decision making
Keywords : Agroecology, plant use, Formal Concept Analysis, Implication rule, Visualization
For a farmer, implementing agroecological practices on his farm requires having a decision support system (DSS) allowing him to identify them. The development of such a DSS requires a sufficiently extensive knowledge base and a knowledge navigation system (SN) adapted to her/his needs, allowing to consider innovative practices, where appropriate.
|Characterization of fruit tree health with RGB imaging using close-range sensing: application to chlorophylle content and to shot hole in peach
Keywords : Close-range sensing, deep learning, machine learning, RGB, horticulture, biotic stress
Developping agro-ecological practices in fruit tree production implies reducing pesticide use, however to date only few cultivars baring disease and pests resistances are available on the market. Quantifying resistances or tolerances among breeding material or genetic resources in the orchard is challenging: because damages are typically assessed visually with ordinal scale and thus lack resolution and preciseness. In addition, only few tools allow measuring integrative traits (tree vigor, photosynthetic activity) in a quick and reliable way. This master thesis will explore whether data acquired with close-range sensing can allow for the characterization of tree health components and whether these methods can be more efficient than usual ones. To this aim, one specific trait, shot hole symptoms (caused by Coryneum beijerinckii), and an integrative trait, chlorophyll content, were chosen. These traits will be measured with standard methods (visually and with a chlorophyll-meter respectively) and using close-range imaging in two peach orchards managed under low phytosanitary protection. Image acquisition will be done with a RGB camera hold on a PHENOMAN pole. The student will participate to data acquisition and to pre-processing to build a reference dataset. He/she will then estimate relevant phenotypic variables using these images. For shot hole, deep learning algorithms will be trained on manually annotated pictures. For chlorophyll content, machine learning methods will link chlorophyll-meter measurements with one or several variables obtained from images. After a critical analysis of the results, the methods elaborated in this project could be deployed to a larger audience via acquisition protocols and analytic pipelines.
|Automatic segmentation of aerial images of agroforestry systems to characterize their structure
Keywords : Image analysis – Classification – Segmentation – Remote sensing – Neural networks – Deep learning – Agroforestry
The ecosystem services provided by agroforestry systems are now widely recognized (biodiversity, carbon storage, etc.). However, the richness of biodiversity is difficult to characterize exhaustively in time and space, as is the capacity to store carbon. These quantities are particularly dependent on the structure of an agroforestry system, i.e. the composition and arrangement of the plant resources that make it up.