AI for High-Throughput Forest Phenotyping – Dropheno Project

In collaboration with the University of Perpignan (UPVD) and multiple research partners

The Dropheno project, part of the Labex TULIP Innovation initiative, aims to develop high-throughput phenotyping strategies for beech forests by integrating drone-based imaging, artificial intelligence, and field ecology.

The goal is to enable precise monitoring of forest structure, health, and adaptive responses, providing actionable insights for forest management and conservation.

AI-Driven Phenotyping

BionomeeX contributes its expertise in AI-driven image analysis, supporting the extraction of quantitative traits from high-resolution drone imagery.

This approach allows researchers to:

• 🌿 Measure key phenotypic traits at large scales, including canopy structure, leaf density, and growth patterns

• ⚡ Detect early signs of stress or environmental adaptation

• 📊 Automate data processing from massive image datasets, enabling faster and more reproducible analyses

Integration with Related Research

Beyond Dropheno, the collaboration intersects with several related projects at UPVD, including:

• 🧬 ANR PolyPMD and DYSCORD: studying gene regulation and chromosome dynamics during plant development

• 🌱 POCTEFA FloraLab+: regional biodiversity and forest adaptation monitoring

• 🌳 BioDivOc FAGADAPT: assessing short-term adaptation of temperate forests to climate change

• 🦋 EUPHYDRYAS: multi-omics approaches for conservation of endangered butterflies

• 🔬 Niche modeling and chemical mediation studies: predicting ecological niches and understanding plant-insect interactions

• 📈 Socio-ecological modeling (DySSPO): integrating ecological, social, and economic dynamics in the Pyrenees region

Toward Smarter Forest Management

By combining AI, high-resolution imaging, and ecological modeling, Dropheno exemplifies how technological innovation can enhance the monitoring, conservation, and adaptive management of complex forest ecosystems.

The project enables large-scale, quantitative, and reproducible assessment of forest phenotypes, providing a bridge between field ecology, genomics, and applied environmental science.