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.
To discuss opportunities to join the group, do not hesitate to send us a CV and describe your interest.
*Prior to 2022, publications are selected from Quentin Geissmann’s previous work.
Year | Title | Journal | Authors |
---|---|---|---|
2024 | Machine learning reveals singing rhythms of male Pacific field crickets are clock controlled | Behavioral Ecology | Westwood 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. |
2013 | OpenCFU, a New Free and Open-Source Software to Count Cell Colonies and Other Circular Objects | PLOS ONE | Geissmann |