Dr. Nikolaos Tsoulias

Organizational Unit(s):Department of Technology Professorship for Technology

Contact:
Phone: +49 6722 502 371
eMail: Nikolaos.Tsoulias(at)hs-gm.de

Postal Address:Von-Lade-Straße 1
D-65366 Geisenheim

Address: Building 6003
Room 213
Brentanostraße 9
65366 Geisenheim

Nikos Tsoulias, Senior researcher and lecturer at Hochschule Geisenheim University. Dr. Nikos Tsoulias holds a PhD in Agricultural Engineering from the Agricultural University of Athens (Greece) since 2021. He received his M.Sc. in Precision Farming (2016) at Harper Adams University (United Kingdom) fully funded by CNH Industrial, while he holds a BSc is in Agriculture with an integrated M.Sc. in Agricultural Engineering from Agricultural University of Athens (Greece).

Currently, he works at the Department of Agricultural Engineering at Hochschule Geisenheim University, where he gives lectures on precision agriculture, phenotyping, digital twins, agricultural machines and robotics in horticulture and viticulture. From the beginning of his professional career, he worked as a researcher, and has focused on strong cooperation with the industry and research. He is an expert in machine vision and optical sensors in the agri and food sector.

Dr. Nikos Tsoulias, is the author of over 30 peer-reviewed scientific articles. Whereas, he gave a keynote titled "Bridging Digital Shadows with Physiological Models and Field Applications" at EHC 24 in Bucharest, organized by ISHS. He specialises in conceiving techniques for real-time detection in the field of machine vision and plant sensing. His research interests include fruit and tree phenotyping, 3D data, photonics in agriculture, IoT systems, unmanned ground and aerial vehicles, machinery automation, precision horticulture/viticulture and digital twins. Recently, he has been involved as in several European projects such as the Oenotrace and SHEET projects funded by ICT-AGRI-FOOD Co-Fund, while as well as in national projects such as the ISObot, DIWAKOPTER, AquaC+ and Primefruit.

Poss B., Tsoulias N., Heiß A., Paraforos D. (2024): Vergleich zwischen luft- und bodengeführten Laser-Scannern zur Bestimmung der Laubwandhöhe im Weinbau. Bonn S. 371 - 376.
978-3-88579-738-8

Psiroukis V., Papadopoulos G., Kasimati A., Tsoulias N., Fountas S. (2023): Cotton Growth Modelling Using UAS-Derived DSM and RGB Imagery. Remote Sensing 15 (5) DOI: 10.3390/rs15051214

Miranda J.C., Gené-Mola J., Zude-Sasse M., Tsoulias N., Escolà A., Arnó J., Rosell-Polo J.R., Sanz-Cortiella R., Martínez-Casasnovas J.A., Gregorio E. (2023): Fruit sizing using AI: A review of methods and challenges. Postharvest Biology and Technology 206 DOI: 10.1016/j.postharvbio.2023.112587

Tsoulias N., Saha K.K., Zude-Sasse M. (2023): In-situ fruit analysis by means of LiDAR 3D point cloud of normalized difference vegetation index (NDVI). Computers and Electronics in Agriculture 205 DOI: 10.1016/j.compag.2022.107611

Tsoulias N., Jörissen S., Nüchter A. (2022): An approach for monitoring temperature on fruit surface by means of thermal point cloud. MethodsX 9 DOI: 10.1016/j.mex.2022.101712

Penzel M., Tsoulias N. (2022): Annual shoot growth on apple trees with variable canopy leaf area and crop load in response to LiDAR scanned leaf area to fruit ratio**. International Agrophysics 36 (3) S. 173 - 180. DOI: 10.31545/intagr/150761

Penzel M., Tsoulias N., Saha K.K., Handtke N., Gubin E., Zude-Sasse M. (2022): Cherry tree growth in response to varying soil apparent electrical conductivity. International Society for Horticultural Science (ISHS) S. 289 - 296. DOI: 10.17660/ActaHortic.2023.1366.34

Tsoulias N., Xanthopoulos G., Fountas S., Zude-Sasse M. (2022): Effects of soil ECa and LiDAR-derived leaf area on yield and fruit quality in apple production. Biosystems Engineering 223 S. 182 - 199. DOI: 10.1016/j.biosystemseng.2022.03.007

Tsoulias N., Herppich W.B., Fountas S., Zude-Sasse M. (2022): Estimating the spatial variability of water needs using the soil ECa, rooting depth, and fruit developmental stage in sweet cherry orchard. International Society for Horticultural Science (ISHS) S. 103 - 110. DOI: 10.17660/ActaHortic.2022.1335.12

Saha K.K., Tsoulias N., Weltzien C., Zude-Sasse M. (2022): Estimation of Vegetative Growth in Strawberry Plants Using Mobile LiDAR Laser Scanner. Horticulturae 8 (2) DOI: 10.3390/horticulturae8020090

Zapata N.T., Tsoulias N., Saha K.K., Zude-Sasse M. (2022): Fourier analysis of LiDAR scanned 3D point cloud data for surface reconstruction and fruit size estimation. S. 197 - 202. DOI: 10.1109/METROAGRIFOR55389.2022.9964881

Mostafa H., Saha K.K., Tsoulias N., Zude-Sasse M. (2022): Using LiDAR technique and modified Community Land Model for calculating water interception of cherry tree canopy. Agricultural Water Management 272 DOI: 10.1016/j.agwat.2022.107816

Tsoulias N., Fountas S., Zude-Sasse M. (2021): Estimating the canopy volume using a 2D LiDAR in apple trees. International Society for Horticultural Science (ISHS) S. 437 - 444. DOI: 10.17660/ActaHortic.2021.1327.58

Saha K.K., Tsoulias N., Zude-Sasse M. (2021): Estimation of leaf area of sweet cherry trees trained as spindle using ground based 2D mobile LiDAR system. International Society for Horticultural Science (ISHS) S. 429 - 436. DOI: 10.17660/ActaHortic.2021.1327.57

Penzel M., Herppich W.B., Weltzien C., Tsoulias N., Zude-Sasse M. (2021): Modeling of Individual Fruit-Bearing Capacity of Trees Is Aimed at Optimizing Fruit Quality of Malus x domestica Borkh. 'Gala'. Frontiers in Plant Science 12 DOI: 10.3389/fpls.2021.669909

Zude-Sasse M., Akbari E., Tsoulias N., Psiroukis V., Fountas S., Ehsani R. (2021): Sensing in Precision Horticulture. 978-3-030-78431-7

Saha K.K., Tsoulias N., Zude-Sasse M. (2020): Bewertung der Messunsicherheit bei der Analyse von Obstbäumen mit mobilem 2D-Laserscanner. Landtechnik 75 (4) S. 270 - 277. DOI: 10.15150/lt.2020.3251

Penzel M., Lakso A.N., Tsoulias N., Zude-Sasse M. (2020): Carbon consumption of developing fruit and the fruit bearing capacity of individual RoHo 3615 and Pinova apple trees. International Agrophysics 34 (4) S. 409 - 423. DOI: 10.31545/intagr/127540

Tsoulias N., Xanthopoulos G., Fountas S., Zude M. (2020): In-situ detection of apple fruit using a 2D LiDAR laser scanner. S. 278 - 282. DOI: 10.1109/metroagrifor50201.2020.9277629

Penzel M., Tsoulias N., Herppich W.B., Weltzien C., Zude-Sasse M. (2020): Mapping the fruit bearing capacity in a commercial apple (Malus x domestica BORKH.) orchard. S. 283 - 287. DOI: 10.1109/metroagrifor50201.2020.9277563

Tsoulias N., Gebbers R., Zude-Sasse M. (2020): Using data on soil ECa, soil water properties, and response of tree root system for spatial water balancing in an apple orchard. Precision Agriculture 21 S. 522 - 548. DOI: 10.1007/s11119-019-09680-8

Tsoulias N., Paraforos D.S., Fountas S., Zude-Sasse M. (2019): Calculating the water deficit spatially using LiDAR laser scanner in an apple orchard. S. 115 - 121. DOI: 10.3920/978-90-8686-888-9_13
978-90-8686-337-2

Saha K.K., Tsoulias N., Zude-Sasse M. (2019): Removing ground points from 3D point cloud in apple orchard. Potsdam Leibniz-Institut für Agrartechnik und Bioökonomie e.V. Bornimer Agrartechnische Berichte 102 S. 76 - 83.

Penzel M., Tsoulias N., Lakso A.N., Zude-Sasse M. (2019): Spatial variability of the individual tree's fruit bearing capacity in commercial orchards of 'RoHo 3615' and 'Pinova' apple. International Society for Horticultural Science (ISHS) S. 125 - 132. DOI: 10.17660/ActaHortic.2021.1314.17

Tsoulias N., Paraforos D., Brandes N., Fountas S., Zude-Sasse M. (2018): Calculating the Water Deficit of Apple Orchard by Means of Spatially Resolved Approach. Montreal,Quebec

Tsoulias N., Zude-Sasse M. (2018): Calculating the water deficit of cherry orchard by means of spatially resolved approach. DOI: 10.13031/aim.201800906

Brandes N., Tsoulias N., Zude-Sasse M. (2018): Ermittlung des kultur-und standortangepassten Wasserbedarfs unter Berücksichtigung der Fruchtentwicklungsphasen bei Kulturheidelbeere (Vaccinium corymbosum L.). Landtechnik 73 (6) S. 179 - 187. DOI: 10.15150/lt.2018.3195

Optimised management of field robots through intelligent process control and standardised interoperability mechanisms

Project start: 01.11.2024
Project end: 31.10.2027
Sponsor: Federal Ministry of Food and Agriculture

Autonomous field robots have enormous potential for ecologizing crop production and mitigating labor shortage. Although these systems are now ready for market, there are still major challenges, particularly with regard to their interaction with machine management, process control and the interaction between robot and implement. The aim of ISObot is to develop an integrated system for optimizing the operational management of field robots through improved interoperability at different levels, as well as automated process control. The starting point are commercial robot-implement combinations and a web-based machine management system. A process controller is being developed for weed management in viticulture, which uses sensors and intelligent algorithms to monitor and actively control the work process and document the quality of work. Another use case in arable farming will focus on monitoring the hoeing process and recording process parameters. In both cases, the robots will be linked to the machine management system via an IoT system in order to visualize process-relevant information via user-friendly interfaces and make it available for further agronomic use. Finally, approaches are being developed to bring communication between the subsystems involved into an automated, machine-readable form, primarily on the basis of the ISO 11783 (ISOBUS) standard, thus ensuring maximum interoperability at different levels without additional manual effort. Dissemination and transfer activities will be leveraged in the project to establish an exchange with agricultural practice. Eventually, another aim is to derive recommendations for further standardization efforts to improve the interoperability of electronic communication in agricultural machinery.

Dr. Niko­laos Tsou­lias

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Dr. Nikolaos Tsoulias