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Agriculture generates a significant amount of data today, which is still underutilized due to its heterogeneity and various data collection methods (ground or onboard sensors, remote sensing, etc.). At the same time, many phenomena of interest, such as plant growth and disease development, are based on mechanical, physical, or biological principles. One of the keys to the development of agroecology is the ability to anticipate changes in crops or ecosystems. To achieve this, it is essential to use existing data and models or develop new models (possibly combining traditional approaches with data science) to provide farmers and professionals in advisory roles with precise and efficient decision support tools that complement individual expertise and experience. Participatory tools are included in the description above.

The objective of this axis is to support projects aiming to offer:

• Sensors and data acquisition methods to measure the condition of plants, animals, and the environment, anticipate weather events, as well as control the safety of machinery and prove their reliability ;
• Methods for integrating massive and heterogeneous data ;
• Information systems (system interoperability, efficiency in data storage and access, building secure and participatory information systems, leveraging traceability data for environmental certification, etc.) ;
• Model construction (combining formalized knowledge from existing agronomic models, expert knowledge, and knowledge inferred from data).

The list above is not exhaustive.

Projects funded contributing to this axis include:

• Targeted research projects at the beginning of the program:
  • Pl@ntAgroEco, new perspectives on characterizing plant diseases and species associations based on deep learning and participatory sciences
  • MELICERTES, modeling ecosystem states and dynamics - applications to carbon flux and storage in ecosystems altered by agricultural activities
  • MISTIC, microbiomes of cultivated plants and ICT
• Infrastructure projects:
  • BReIF, an infrastructure to accelerate the use of diverse biological resources
• 2023 Thesis and Postdoctoral Fellowship Winners:
  • "Using multiple object tracking and subject reidentification to elucidate group dynamics and interactions among insects" Ph.D. Thesis
  • "Modeling the dynamics of potato cyst nematodes to optimize the use of trap plants" Postdoctoral Fellowship