The Brown Marmorated Stink Bug Halyomorpha halys (HH) is an emerging pest of global importance for many agricultural crops. Field monitoring is crucial to obtain information on the actual presence and abundance of HH in order to organize timely and proper management actions, also because chemical control is proved to be unsatisfactory. Driven by the quest of improving sustainability, we propose an autonomous field-monitoring system and an autonomous fruit-monitoring system to replace common human-based field monitoring of HH and detect the internally damaged fruits invisible to the naked-eye. This system is based on data collection and processing with new emerging technologies (Unmanned Aerial Vehicles, Vision sensors, spectroscopy, Edge Computing, LPWAN Communications, NIR-HSI, microwave/THz) enhanced by Algorithmic and Machine Learning techniques. The innovations achieved by this project will become the foundations for similar monitoring systems for other insect pests (e.g., the carrot fly and many more). A third-party trusted logbook of the leaded activities will be implemented, which will be also an independent outcome of the project. By integrating multidisciplinary competences, the project will empower farmers, advisors, and phytosanitary personnel for obtaining a reliable monitoring system that saves time, energy, and costs allowing to control the pest in a timely manner, reducing the number of treatments towards increasing sustainability of agroecosystem management. The end-consumers will benefit from a higher quality marketable fruit, and larger transparency on the fruit-production chain. The project will increase scientific knowledge posing the basis of an epidemiological model for HH. Open data will be made available via cloud repository to the scientific community.
The project HALY.ID has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 862665 ERA-NET ICT-AGRI-FOOD (HALY-ID 862671)
National Funding: Bundesministerium für Bildung und Forschung (BMBF)
Förderkennzeichen: 031B1093
Project duration: 02/2021 to 01/2024
| Name | Phone | Room | |
|---|---|---|---|
| Lennart Almstedt | almstedt[[at]]ibr.cs.tu-bs.de | +49-531-3913285 | 133 |
| David Niederprüm | niederpruem[[at]]ibr.cs.tu-bs.de | +49-531-3913249 | 134 |
| Jan Schlichter | schlichter[[at]]ibr.cs.tu-bs.de | +49-531-3913154 | 118 |
| Dr.-Ing. Sven Pullwitt | pullwitt[[at]]ibr.cs.tu-bs.de | ||
| Prof. Dr.-Ing. Lars Wolf | wolf[[at]]ibr.cs.tu-bs.de | +49-531-3913288 | 138 |
| Title | Type | Supervisor | Status |
|---|---|---|---|
| Dynamic Routing Decisions in UAV-Aided WSNs | Master Thesis | Lennart Almstedt | finished 2023 |
| Effizienter Datenzugriff auf der Blockchain | Bachelor Thesis | Lennart Almstedt | finished 2024 |
| Sicheres Auslesen von Sensordaten | Bachelor Thesis | Lennart Almstedt | finished 2023 |
| Extending a Smart Contract Framework with Access Control for private Data | Bachelor Thesis | Lennart Almstedt | finished 2023 |
| Machine Learning in Webassembly basierten Smart Contracts | Bachelor Thesis | Lennart Almstedt | finished 2023 |
| Porting an SGX-Based Smart Contract Framework to ARM TrustZone | Bachelor Thesis | Lennart Almstedt | finished 2023 |
| Implementation and evaluation of wireless connectivity for an image recognition system | Project Thesis | David Niederprüm | finished 2023 |
| Framework für die Perfomance-Messung von TrustZone-Anwendungen | Bachelor Thesis | Lennart Almstedt | |
| HALY.ID: Sensorknotenentwicklung | HiWi Job | Lennart Almstedt | finished |
If you are interested in writing a thesis regarding this project, please feel free to contact Prof. Dr. Rüdiger Kapitza, or Prof. Dr.-Ing. Lars Wolf.