Research

ON-Cerco (Active)

The standard view when using drones is looking straight down (nadir). Oblique viewing angles allow adding more information that can be used for plant disease incidence and severity estimates.

ReflectDetect (Active)

An innovative, fully automated command line software for in-flight radiometric calibration of UAV-mounted 2D snapshot multi-camera imagery. For more information please visit the online repository.

Remotely sensed leaf area index can improve mechanistic Cercospora beticola epidemiological models for disease predictions (Active)

Plant disease epidemiological models, empirical and mechanistic, assist with disease management decisions by providing a prediction of plant disease epidemics. Yet, adoption of predictive models in integrated crop protection can be low as available decision support systems require validation, in-field scouting by growers and agronomists and, from time to time, model parameter reviews. This project investigates, whether remote sensing technologies and automation strategies can assist in this process and ease adoption.

High interval measurement of leaf angle dynamics using stereo vision (Active)

Leaf inclination angles (LIA) are crucial for regulating various processes within the plant carbon–water–energy nexus. In agriculture, these processes include photosynthesis, leaf temperature, plant growth, and the microclimate within the canopy. Despite its significance, LIA remains one of the most understudied plant functional traits due to the difficulty of measuring it accurately, particularly at short temporal intervals. In optical remote sensing, LIA is a key factor influencing spectral variability, which directly impacts the robustness of empirical models. Together with Frederik Hennecke, a data science student at the University of Göttingen, we developed a novel and automated approach to capture 3D point clouds of small crops (e.g., sugar beet), enabling precise determination of LIA from the generated data. Our system, controlled by publicly available code, and therefore adhering to FAIR principles and open software standards, allows for customizable capture intervals and positions. The method was validated using a 3D-printed sugar beet model with known LIA, demonstrating its potential for advancing plant trait studies.

The FarmBot Genesis v1.6 was upgraded with a Raspberry Pi (A) to control the position (e.g., yellow dots) and interval of image capture events of an Intel RealSense D405 (B) depth camera. The camera was mounted at the bottom of the z-axis extrusion, while the associated Raspberry Pi was mounted above the electronic box on the gantry of the FarmBot. The camera generated 3-dimensional point clouds that served as a source for LIA retrieval. The accuracy of LIA measurement was validated with a 3D-printed plant with known LIA´s.

Evaluating spot-spraying systems in sugar beet (Completed)

Across multiple field site in northern Germany, it is investigated whether spot spraying can contribute to herbicide savings in sugar beet cultivation.

agrIR - Developing R programming tools for crop water stress detection and energy balance modelling (Active)

We are working alongside Rikard Graß (UFZ), Hannah Boedeker (UFZ), and Marco Hofmann (HS Geisenheim) to develop software tools aimed at enhancing thermography applications in agricultural datasets.

Visiting PhD Student Research (Active) - Investigating the influence of leaf angle dynamics on empirical plant disease modelling.

Giuseppe Quaratiello is conducting his research on diurnal sugar beet leaf dynamics and their impact on empirical classification models for structural and biochemcial plant traits. Giuseppe is enrolled at the University of Pisa in Italy and supervised by Lorenzo Cotrozzi.

M.Sc. Research (Active) - Investigating the influence of leaf angle dynamics on empirical plant disease modelling.

Marco Andres Paucar is conducting his research on diurnal sugar beet leaf dynamics and their impact on empirical classification models for structural and biochemcial plant traits. Andrés is enrolled in the M.Sc. program Sustainable International Agriculture at the Georg-August-Universität Göttingen.

M.Sc. Research (Alumni) - Digital technologies for Virus Yellows phenotyping in sugar beet across spatial scales.

Nathan Okole is investigating whether optical sensors at close-range and from uncrewed aerial systems (UAS) can be used for phenotyping Virus Yellows in sugar beet. Nathan is enrolled in the M.Sc. program Crop Protection at the Georg-August-Universität Göttingen.

M.Sc. Research (Alumni) - Accuracy assessment for spot-spraying systems.

Teresa Starck assessed the precision and practicability of a spot-spraying system for use in maize and sugar beet crops. After her thesis, Teresa accepted a position at Xarvio - BASF Digital Farming GmbH.

B.Sc. Research (Alumni) - Can we predict the susceptibility to myrtle rust with the help of physical leaf traits?

Larissa Krey collected, and statistically associated, various - biologically relevant - plant functional traits to the pathosystem Austropuccinia psidii on Backhousia citridora.