The DARSA Group

Digital Approaches for Resilient and Sustainable Agriculture

Aarhus University

Center for Quantitative Genetics and Genomics

About us

The Digital Approaches for Resilient and Sustainable Agriculture (DARSA) group is part of the Center for Quantitative Genetics and Genomics (QGG), at Aarhus University. Within DARSA, we develop and apply remote-sensing tools and innovative open-source machine-learning methods to make agroecosystems more productive, sustainable and resilient. We collaborate with other members of the QGG as well as with other researchers in Aarhus and worldwide to target both the breeding and production sides of agriculture. Amid severe environmental crises, we aim to lead a new, digital and sustainable, green transition. The DARSA group recently started, and we are actively looking for collaborators and new members, so do not hesitate to contact us.

Interests

🧠 Development of Deep-Learning Tools
🐛 Automatic Monitoring of Agricultural Pests
🌱 High-Throughput Plant Phenotyping
🌳 Agroecology and Biodiversity

People

Sep 1, 2022

Quentin Geissmann

Principal Investigator

Jan 6, 2025

Zixuan He

Postdoc

Aug 1, 2024

Helena Russello

Postdoc

Dec 1, 2023

Marta Gomez

PhD Student

Jun 1, 2023

Melika Baghooee

PhD Student

Sep 1, 2022

Daphne Chevalier

Master Student

Projects

Dec 1, 2024

DWARPH

AI-inspired paradigm to study plant response surfaces

Nov 29, 2023

ANTENNA

MAkiNg Technology work for moNitoring polliNAtors

Nov 28, 2023

Global Worming

Monitoring earthworms with AI and remote sensing

Feb 15, 2021

Sticky Pi

An AI-powered smart insect trap for community chronoecology

Jan 11, 2015

Ethoscope

A platform to monitor behaviour of small animals in real-time

Jan 21, 2013

OpenCFU

An open-source software to count bacterial colonies

Opportunities

If you have project ideas and are motivated, we list below a number of options to obtain funding and we would be happy discuss and possibly support you. Send us a CV and describe your interest.

HFSP postdoctoral fellowship program

High-risk, interdisciplinary and international projects on fundamental biology.

Dec 1, 2024

Marie Skłodowska-Curie Actions postdoctoral fellowship

High-risk, interdisciplinary and international scientific projects.

Dec 1, 2024

Publications

Year	Title	Journal	Authors
2024	Machine learning reveals singing rhythms of male Pacific field crickets are clock controlled	Behavioral Ecology	Westwood et al.
2024	Towards a standardized framework for AI-assisted, image-based monitoring of nocturnal insects	Philosophical Transactions of the Royal Society B: Biological Sciences	Roy et al.
2024	Order among chaos: high throughput MYCroplanters can distinguish interacting drivers of host infection in a highly stochastic system	bioRxiv	Chen et al.
2024	Computer vision and deep learning in insects for food and feed production: A review	Computers and Electronics in Agriculture	Nawoya et al.
2023	Hierarchical classification of insects with multitask learning and anomaly detection	Ecological Informatics	Bjerge et al.
2022	Sticky Pi is a high-frequency smart trap that enables the study of insect circadian activity under natural conditions	PLOS Biology	Geissmann et al.
2019	Most sleep does not serve a vital function: Evidence from Drosophila melanogaster	Science Advances	Geissmann et al.
2019	Rethomics: An R framework to analyse high-throughput behavioural data	PLOS ONE	Geissmann et al.
2017	Ethoscopes: An open platform for high-throughput ethomics	PLOS Biology	Geissmann et al.
2016	MEANS: python package for Moment Expansion Approximation, iNference and Simulation	Bioinformatics	Fan et al.
2015	Dynamics of Copy Number Variation in Host Races of the Pea Aphid	Molecular Biology and Evolution	Duvaux et al.
2013	OpenCFU, a New Free and Open-Source Software to Count Cell Colonies and Other Circular Objects	PLOS ONE	Geissmann