Publikationen

Francis, L., Geissler, L., Okole, N., Stachniss, C., Gipp, B.,Heim, R. HJ. (2025). ReflectDetect: A software tool for AprilTag-guided in-flight radiometric calibration for UAV-mounted 2D snapshot multi-camera imagery. SoftwareX. 10.1016/j.softx.2025.102150.

Hennecke, F., Bömer J.,Heim, R. HJ. (2025). Modification of an Automated Precision Farming Robot for High Temporal Resolution Measurement of Leaf Angle Dynamics using Stereo Vision. MethodsX. doi: 10.1016/j.mex.2025.103169.

Heim, R. HJ., Melloy, P., Okole, N., Ispizua Yamati, F. R., Mahlein, A.‑K. (2024). Remotely sensed leaf area index can improve mechanistic Cercospora beticola epidemiological models for disease predictions. (in review)

Heim, R.HJ., Okole, N., Steppe, K. Van Labeke, M.C., Geedicke, I., Maes, W.H. (2024). An applied framework to unlocking multi-angular UAV reflectance data: a case study for classification of plant parameters in maize (Zea mays). Precision Agriculture. doi:10.1007/s11119-024-10133-0

Okole, N., Ispizua, F. R. Y., Hossain, R., Varrelmann, M., Mahlein, A.‑K., & Heim, R. HJ. (2024). Leveraging aerial low‑altitude remote sensing and deep learning for in‑field disease incidence scoring of virus yellows in sugar beet.
Plant Pathology. doi:https://doi.org/10.1111/ppa.13973

Yamati, F. R. I.,Heim, R. HJ., Günder, M., Gajda, W., & Mahlein, A.‑K. (2023). Image‑to‑image translation for satellite and UAV remote sensing: A use case for Cercospora Leaf Spot monitoring on sugar beet. 2023 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), 783–787. doi: 10.1109/MetroAgriFor58484.2023.10424276

Okole, N., Yamati, F. R. I., Hossain, R., Varrelmann, M., Mahlein, A.‑K., & Heim, R. HJ. (2023). Hyperspectral signatures and betalain indicator for beet mosaic virus infection in sugar beet. 2023 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), 506–511. doi: 10.1109/MetroAgriFor58484.2023.10424290

Vlaminck, M., Diels, L., Philips, W., Maes, W. H.,Heim, R. HJ., Wit, B. D., & Luong, H. Q. (2023). A multisensor uav payload and processing pipeline for generating multispectral point clouds. Remote Sensing, 15, 1524. doi:10.36961/si28804

Heim, R. HJ., Streit, S., Koops, D., Kuska, M. T., & Paulus, S. (2022). Digital weed management – new trends for weed scoring in sugar beet. Sugar Industry, 147, 343–351. doi: 10.36961/si28804

Kuska, M. T.,Heim, R. HJ. (shared first author), Geedicke, I., Gold, K. M., Brugger, A., & Paulus, S. (2022). Digital plant pathology: A foundation and guide to modern agriculture. Journal of Plant Diseases and Protection, 129, 457–468. doi: 10.1007/s41348-022-00600-z

Mahlein, A., Behmann, J., Bohnenkamp, D.,Heim, R. HJ., Streit, S., & Paulus, S. (2022). Automated assessment of plant diseases and plant traits by sensors – how can digital technologies support smart farming and plant breeding? In Advances in plant phenotyping for more sustainable crop production. (pp. 390–404). Burleigh Dodds Science Publishing. doi: 10. 19103/AS.2022.0102.17.

Mahlein, A.‑K., Heim, R. HJ., Brugger, A., Gold, K. M., Li, Y., Bashir, A. K., Paulus, S., & Kuska, M. T. (2022). Special issue: Digital plant pathology for precision agriculture. Journal of Plant Diseases and Protection, 129, 455–456. doi: 10.1007/ s41348-022-00620-9.

Ajamian, C., Chang, H.‑C., Tomkins, K., Farebrother, W.,Heim, R. HJ., & Rahman, S. M. A. (2021). Identifying invasive weed species in alpine vegetation communities based on spectral profiles. Geomatics, 1, 177–191. doi: 10.3390/GEOMATICS1020011

Funghi, C.,Heim, R. HJ., Schuett, W., Griffith, S. C., & Oldeland, J. (2020). Estimating food resource availability in arid environments with sentinel 2 satellite imagery. PeerJ, 8, e9209. doi: 10.7717/peerj.9209

Heim, R. HJ., Carnegie, A. J., & Zarco‑Tejada, P. J. (2019). Breaking down barriers between remote sensing and plant pathology. Trop‑ ical Plant Pathology, 44, 398–400. doi: 10.1007/s40858-019-00300-4

Heim, R. HJ., Wright, I. J., Allen, A. P., Geedicke, I., & Oldeland, J. (2019). Developing a spectral disease index for myrtle rust (Austrop‑ uccinia psidii). Plant Pathology, 68, 738–745. doi: 10.1111/ppa.12996

Heim, R. HJ., Wright, I. J., Scarth, P. F., Carnegie, A. J., Taylor, D. B., & Oldeland, J. (2019). Multispectral, aerial disease detection for myrtle rust (Austropuccinia psidii) on a lemon myrtle plantation. Drones, 3, 25. doi: 10.3390/drones3010025

Heim, R. HJ., Wright, I. J., Chang, H.‑C., Carnegie, A. J., Pegg, G. S., Lancaster, E. K., Falster, D. S., & Oldeland, J. (2018). Detecting myrtle rust (Austropuccinia psidii) on lemon myrtle trees using spectral signatures and machine learning. Plant Pathology, 67, 1114–1121. doi: 10.1111/ppa.12830

Falter, C., Ellinger, D., von Hülsen, B.,Heim, R. HJ., & Voigt, C. A. (2015). Simple preparation of plant epidermal tissue for laser mi‑ crodissection and downstream quantitative proteome and carbohydrate analysis. Frontiers in Plant Science, 6, 194. doi: 10.3389/fpls.2015.00194

Heim, R. HJ., Jürgens, N., Große‑Stoltenberg, A., & Oldeland, J. (2015). The effect of epidermal structures on leaf spectral signatures of ice plants (Aizoaceae). Remote Sensing, 7, 16901–16914. doi: 10.3390/rs71215862