🐞 AI for Pest and Parasitoid Interaction Analysis

In collaboration with a CBGP research team specialized in population biology and ecology

The NGS-OLICIT project (funded by ANR) aims to prevent the impact of pests on citrus and olive crops by analyzing the interactions between pest species and their parasitoids.

BionomeeX develops AI-based image analysis and classification tools to:

• Automatically detect species

• Map ecological relationships

• Support the interpretation of genomic and ecological data

By combining deep learning with pattern recognition, this approach provides new insights into biological control strategies and enhances data-driven decision-making for sustainable agriculture.

🐜 AI for Small Insect Detection and Classification

In collaboration with a CBGP team specialized in insect taxonomy and systematics

This project tests the feasibility of using AI to detect and classify small insects, addressing challenges linked to their size and morphological diversity.

Using HiXloop, BionomeeX applies deep learning and computer vision to high-resolution imagery to:

• Automatically distinguish species

• Extract morphological descriptors

This project advances automated taxonomy and supports large-scale biodiversity studies by providing a faster, standardized, and reproducible method for species identification.

🐞 AI for Studying Ladybug Color Polymorphism

In collaboration with a CBGP team specialized in population genetics

BionomeeX collaborates with CBGP to investigate the geographic distribution and color morphs of ladybugs.

The research aims to determine whether color variation is influenced by:

• Genetic markers

• Environmental conditions

• Sex-linked factors

By integrating genomic analysis, AI-based image phenotyping, and pattern recognition, this project explores the evolutionary and ecological mechanisms underlying phenotypic diversity and adaptive traits.