AI and the Environment: Real-World Applications from Biodiversity to Climate Adaptation

Environmental research is facing a data crisis. Scientists monitoring ecosystems, forests, insect populations, and climate adaptation now collect more data than they can manually process from drone imagery covering thousands of hectares to genomic datasets with hundreds of thousands of genetic variants. The gap between data collection and meaningful insight is widening.

Artificial intelligence closes that gap. At Bionomeex, a CNRS and University of Montpellier spin-off, we have spent the last five years building AI systems specifically designed for life sciences and environmental research. This article documents what that looks like in practice not in theory, but through active scientific projects running today across ecology, biodiversity, forest science, and climate genomics.

Protecting Wildlife: AI for Bird Detection Near Wind Farms

One of the most direct conflicts between renewable energy development and biodiversity is bird mortality near wind turbines. Protected species raptors, migratory birds are at risk from rotor strikes, and the challenge of monitoring large wind farm perimeters continuously is beyond what manual surveillance can achieve.

In collaboration with BioDivWind and Klanik, Bionomeex co-developed an AI-powered real-time monitoring system that addresses this directly. The system uses computer vision and deep learning applied to live video feeds to detect bird presence near turbines, identify protected species, and track flight paths to assess collision risk.

When a protected bird is detected on an approach trajectory, the system generates an immediate alert allowing operators to temporarily adjust turbine activity to prevent a strike. This is not post-hoc analysis. It is live, automated environmental protection integrated directly into wind farm operations.

The broader implication is significant. As wind energy infrastructure scales across Europe, AI-based monitoring systems like this one offer a practical path to balancing clean energy production with legal obligations around protected species. Manual monitoring at this scale is not economically viable. AI makes continuous coverage possible.

→ Read the full project: AI for Bird Protection in Wind Farms

Understanding Forests: AI for Phenotyping, Drones, and Climate Resilience

Forests are among the most complex and data-rich environments on Earth. Understanding how trees respond to environmental stress, adapt to climate change, and vary genetically across populations requires both large-scale observation and fine-grained analysis. AI is now making both possible simultaneously.

Drone-Based Forest Monitoring - The DropHeno Project

In collaboration with the University of Perpignan (UPVD) and partners from the Labex TULIP Innovation initiative, Bionomeex is contributing AI-driven image analysis to the DropHeno project a high-throughput phenotyping programme for beech forests.

Drones capture high-resolution imagery across large forest areas. Our AI systems then extract quantitative traits from that imagery: canopy structure, leaf density, growth patterns, and early signs of environmental stress. What would take field teams weeks to measure manually, the pipeline produces in hours at a scale and reproducibility that opens entirely new possibilities for forest ecology.

The project connects to a wider network of environmental research at UPVD, including studies on forest adaptation to climate change (BioDivOc FAGADAPT), regional biodiversity monitoring (POCTEFA FloraLab+), and conservation genetics for endangered butterfly species (EUPHYDRYAS).

→ Read the full project: AI for High-Throughput Forest Phenotyping — DropHeno

Decoding the Genetics of Forest Adaptation — CEFE Collaboration

Monitoring forest phenotypes from the air tells you what trees look like. Understanding why they respond differently to drought, heat, or disease requires going deeper into the genome.

In collaboration with the Centre d'Écologie Fonctionnelle et Évolutive (CEFE) in Montpellier, Bionomeex is applying its 2D-GWAS technology to beech tree populations. The analysis targets five key phenotypic traits linked to forest adaptation, with plans to expand to twenty additional traits.

The datasets involved are substantial: up to 300 individual trees, 450,000 genetic variants, and approximately one terabyte of interaction data per phenotype. Our in-house pipeline processes this entirely, and results are made accessible through our Luciol visualization interface allowing CEFE researchers to interactively explore billions of genetic interaction signals in a readable, interpretable format.

The scientific goal is to identify the genetic combinations the epistatic interactions that make certain beech populations more resilient to climate change than others. Those insights directly inform forest conservation and management strategies under conditions of environmental pressure.

→ Read the full project: 2D-GWAS in Beech Trees — CEFE Collaboration

Monitoring Insects and Biodiversity: From Crop Protection to Evolutionary Biology

Insects are both the most diverse group of organisms on Earth and among the most difficult to study at scale. Their size, morphological complexity, and sheer number make manual observation and classification impractical for large biodiversity studies. AI changes the equation.

Protecting Crops Through Biological Control - NGS-OLICIT

The NGS-OLICIT project, funded by the ANR (French National Research Agency) and carried out with a CBGP research team specializing in population biology and ecology, aims to reduce pesticide use on citrus and olive crops by improving biological control strategies.

Bionomeex develops AI-based image analysis and classification tools for this project that automatically detect pest species and their parasitoids from image data, map the ecological relationships between them, and support interpretation of combined genomic and ecological datasets. The goal is to give researchers and agronomists a data-driven understanding of which parasitoid species most effectively control which pest populations under which conditions enabling more targeted and sustainable interventions.

Automated Taxonomy for Large-Scale Biodiversity Studies

In a separate collaboration with a CBGP team specializing in insect taxonomy, Bionomeex is testing the feasibility of using deep learning and computer vision to automatically detect and classify small insects from high-resolution imagery using HiXloop our system for high-resolution image processing.

The challenge is real: small insects present extreme morphological diversity at minimal scale. The system extracts morphological descriptors automatically, distinguishes species without manual intervention, and provides a standardized, reproducible method that can be applied across large image datasets from biodiversity monitoring programmes.

Ladybug Color Morphs and the Genetics of Adaptation

In a third CBGP collaboration, Bionomeex combines AI-based image phenotyping with genomic analysis to study the geographic distribution of ladybug color morphs. The research investigates whether observed color variation is driven by genetic markers, environmental conditions, or sex-linked factors integrating population genetics and AI-based pattern recognition to explore the evolutionary mechanisms behind adaptive phenotypic diversity.

This type of work sits at the intersection of ecology, evolutionary biology, and computer vision and illustrates how AI enables research questions that would otherwise require prohibitive manual effort.

→ Read the full project: AI for Pest and Parasitoid Interaction Analysis

The Technical Capabilities Behind Environmental AI

Each of the projects described above draws on a specific set of AI capabilities that Bionomeex has developed and applied across different environmental contexts. Understanding these capabilities helps clarify what AI can actually contribute to environmental science and what it requires to work well.

Detection identifies biological entities of interest in images a bird near a turbine, an insect on a leaf, a tree exhibiting stress with speed and precision that manual review cannot match at scale.

Segmentation delineates regions of interest precisely: isolating a canopy from background, outlining individual cells in a soil sample, separating species in a mixed-population image. Segmentation turns raw imagery into quantifiable data.

Quantification extracts measurements: surface areas, population counts, morphological parameters, intensity values. This is where image analysis becomes data that can be published, modelled, or acted upon.

Tracking follows objects across image sequences tracing a bird's flight path across multiple frames, following a cell division under a microscope, monitoring organism movement in a field environment over time.

2D-GWAS goes beyond imaging entirely, analyzing the genetic architecture of environmental traits at a scale and resolution that no prior approach could achieve identifying the epistatic interactions that drive adaptation, resilience, and vulnerability in plant and animal populations.

None of these tools work in isolation. The most robust environmental AI systems like the ones Bionomeex builds combine multiple capabilities with scientific domain expertise and, critically, a Human-in-the-Loop validation approach where AI outputs are continuously reviewed and refined by the ecologists and geneticists who understand the biology.

→ Learn more about our AI image analysis capabilities: AI for Biological Image Analysis

Frequently Asked Questions

Can AI be used for real-time environmental monitoring? Yes. The BiodivWind project is a direct example, our system detects and identifies birds near wind turbines in real time, enabling immediate operational responses. Real-time AI monitoring is becoming viable for a growing range of environmental use cases as computer vision and edge computing infrastructure improve.

What types of environmental data can Bionomeex's AI process? We work with drone imagery, field photography, high-resolution microscopy, aerial and satellite data, and genomic datasets. If it is a visual or genomic dataset from a biological or environmental context, we can likely build a useful analytical pipeline for it.

How does 2D-GWAS contribute to environmental science? By identifying interactions between genetic variants rather than just individual variants 2D-GWAS can reveal the genetic basis of complex traits like climate resilience, drought tolerance, and adaptive morphology in plant and animal populations. This has direct applications in forest conservation, agricultural adaptation, and biodiversity research.

Is AI for ecology accessible to smaller research teams or institutions? Yes. Several of our environmental collaborations are with regional universities and mid-sized research teams not just large national institutions. We scope our tools to the data and resources available, and often start with a proof-of-concept phase that produces results on a small dataset before committing to larger pipelines.

Where is Bionomeex based and can you work on field projects outside France? We are headquartered in Montpellier, France, but our collaborations span Europe and North America. Field deployment capability depends on the project we assess this case by case.

From Data to Conservation

The environmental challenges of the coming decades biodiversity loss, climate-driven ecosystem disruption, the pressure to scale renewable energy without sacrificing wildlife are fundamentally data problems. The limiting factor is not observation. It is analysis.

Bionomeex builds AI systems that turn environmental data into scientific insight and operational decisions. From beech forests in Montpellier to wind farms across France, from insect taxonomy to tree genomics, our tools are active in the field today developed with the rigor of peer-reviewed science and the practicality of real deployment.

If you are working on an environmental research project that involves image data or genomic data at scale, we would like to hear about it.

→ Contact Bionomeex