Leiter des Kompetenzzentrums für Digitale Agrarwirtschaft
Studiendekan Studiengang Agrartechnik
Praxisbeauftragter Studiengang Agrartechnik
Digitalisierungsbeauftragter der HSWT
Fakultät
Fakultät Landwirtschaft, Lebensmittel und Ernährung
Publikationen
Zeitschriftenbeiträge
Robin Kümmerer,
Prof. Dr. Patrick Noack,
Prof. Dr. Bernhard Bauer
Non-destructive in-season grain yield (GY) prediction would strongly facilitate the selection process in plant breeding but remains challenging for phenologically and morphologically diverse germplasm, notably under high-yielding conditions. In recent years, the application of drones (UAV) for spectral sensing has been established, but data acquisition and data processing have to be further improved with respect to efficiency and reliability. Therefore, this study evaluates the selection of measurement dates, sensors, and spectral parameters, as well as machine learning algorithms. Multispectral and RGB data were collected during all major growth stages in winter wheat trials and tested for GY prediction using six machine-learning algorithms. Trials were conducted in 2020 and 2021 in two locations in the southeast and eastern areas of Germany. In most cases, the milk ripeness stage was the most reliable growth stage for GY prediction from individual measurement dates, but the maximum prediction accuracies differed substantially between drought-affected trials in 2020 (R2 = 0.81 and R2 = 0.68 in both locations, respectively), and the wetter, pathogen-affected conditions in 2021 (R2 = 0.30 and R2 = 0.29). The combination of data from multiple dates improved the prediction (maximum R2 = 0.85, 0.81, 0.61, and 0.44 in the four-year*location combinations, respectively). Among the spectral parameters under investigation, the best RGB-based indices achieved similar predictions as the best multispectral indices, while the differences between algorithms were comparably small. However, support vector machine, together with random forest and gradient boosting machine, performed better than partial least squares, ridge, and multiple linear regression. The results indicate useful GY predictions in sparser canopies, whereas further improvements are required in dense canopies with counteracting effects of pathogens. Efforts for multiple measurements were more rewarding than enhanced spectral information (multispectral versus RGB).
Mehr
Prof. Dr. Peter Breunig,
Steffen Kümmerer,
Prof. Dr. Bernhard Bauer,
M.Sc. Kevin Braun,
B.Eng. Andreas Fleischmann,
Tobias Meyer,
Prof. Dr. Patrick Noack,
M.Sc. Muhammad Saeed
The operation mode determines the optimal settings for different parameters of agricultural vehicles. A classification of road mode and field mode is essential for adapting settings, e.g. the automatic adjustment of tire inflation during transport on road and operational field work. This study focuses on the development and application of algorithms for automatically detecting the operation mode of agricultural vehicles based on GNSS data. The approach is solely based on the parameters speed, COG and derived values such as acceleration, curve radius and angular speed. Known field boundaries and the current position of the vehicle have been neglected to increase the flexibility and applicability of the algorithm. For this purpose the GNSS data were collected with two GNSS receivers differing with respect to model and correction data source (EGNOS and RTK). Speed, time, heading and derived parameters were included in the development of a decision tree based model to classify the operating mode using the rpart package in RStudio. The prediction of operating mode was carried out with the predict package in RStudio. A confusion matrix was introduced to validate the performance of different models. The algorithms derived from the two training datasets (EGNOS and the RTK dataset) show convincing results in the detection of road and field mode. Both algorithms demonstrated an accuracy of more than 90%. The prediction performance was improved when training and validation data were derived from the same dataset (either EGNOS or RTK dataset). The comparison of two algorithms based on EGNOS and RTK data reveal the advantage of models based on RTK data. It is of great importance that the number of wrong decisions regarding the detection of the operating mode on the road are minimized since road safety plays an important role and the potential harm caused by a wrong decision is substantially higher than in the field. The method reveals a large potential for other applications where the operating mode is relevant.
Mehr
M.Sc. Rolf Wilmes,
Prof. Dr. Bernhard Bauer,
M.Sc. Kevin Braun,
Prof. Dr. Peter Breunig,
B.Eng. Andreas Fleischmann,
Tobias Meyer,
Prof. Dr. Patrick Noack
Wirklich reif für die Praxis? Teil 1: Wie praxistauglich ist die teilflächenspezifische N-Düngung bereits? (2020) Bayrisches Landwirtschaftliches Wochenblatt (18), S. 36-38.
M.Sc. Rolf Wilmes,
Prof. Dr. Bernhard Bauer,
M.Sc. Kevin Braun,
Prof. Dr. Peter Breunig,
B.Eng. Andreas Fleischmann,
Tobias Meyer,
Prof. Dr. Patrick Noack
Hier mehr, dort weniger Maiskörner (2020) Bayrisches Landwirtschaftliches Wochenblatt (22), S. 38-40.
M.Sc. Rolf Wilmes,
Prof. Dr. Bernhard Bauer,
M.Sc. Kevin Braun,
Prof. Dr. Peter Breunig,
B.Eng. Andreas Fleischmann,
Tobias Meyer,
Prof. Dr. Patrick Noack
Feldgrenzen auf den Zentimeter bestimmen (2020) Bayrisches Landwirtschaftliches Wochenblatt (27), S. 41-42.
M.Sc. Rolf Wilmes,
Prof. Dr. Bernhard Bauer,
M.Sc. Kevin Braun,
Prof. Dr. Peter Breunig,
B.Eng. Andreas Fleischmann,
Tobias Meyer,
Prof. Dr. Patrick Noack
In friedlicher Mission (2020) Bayrisches Landwirtschaftliches Wochenblatt (32), S. 38-39.
B.Eng. Andreas Fleischmann,
Prof. Dr. Bernhard Bauer,
M.Sc. Kevin Braun,
Prof. Dr. Peter Breunig,
Tobias Meyer,
Prof. Dr. Patrick Noack,
M.Sc. Muhammad Saeed,
M.Sc. Rolf Wilmes
Der smarte Kuhstall (2020) Bayrisches Landwirtschaftliches Wochenblatt (35), S. 41.
Thomas Göggerle,
M.Sc. Rolf Wilmes,
Prof. Dr. Bernhard Bauer,
M.Sc. Kevin Braun,
Prof. Dr. Peter Breunig,
B.Eng. Andreas Fleischmann,
Tobias Meyer,
Prof. Dr. Patrick Noack
Precision-Farming: teilflächenspezifisch Düngen - so starten Sie (2020) agrarheute, 26. Juni 2020 .
Thomas Göggerle,
M.Sc. Rolf Wilmes,
Prof. Dr. Bernhard Bauer,
M.Sc. Kevin Braun,
Prof. Dr. Peter Breunig,
B.Eng. Andreas Fleischmann,
Tobias Meyer,
Prof. Dr. Patrick Noack
Precision-Farming: Warum teilflächenspezifisch wirtschaften? (2020) agrarheute, 19. Juni 2020 .
Norbert Bleisteiner,
S. Hamberger,
Markus Heinz,
Tina Steigerwald,
K. Seubert,
Prof. Dr. Bernhard Bauer,
Stefan Bauer,
Prof. Dr. Patrick Noack
M.Sc. Rolf Wilmes,
Prof. Dr. Bernhard Bauer,
M.Sc. Kevin Braun,
Prof. Dr. Peter Breunig,
B.Eng. Andreas Fleischmann,
Tobias Meyer,
Prof. Dr. Patrick Noack,
M.Sc. Muhammad Saeed
Beiträge in Monografien, Sammelwerken, Schriftenreihen
Thomas Herlitzius,
Prof. Dr. Patrick Noack,
Jan Späth,
Roland Barth,
Sjaak Wolfert,
Ansgar Bernardi,
Ralph Traphöner,
Daniel Martini,
Martin Kunisch,
Matthias Trapp,
Roland Kubiak,
Djamal Guerniche,
Daniel Eberz-Eder,
Julius Weimper,
Katrin Jakob
Langfristig ist aus der Kooperation des Kompetenzzentrums für Digitalisierung in der Agrarwirtschaft (KoDA) und dem Zentrum für Studium und Didaktik (ZSD) die Konzeption mehrerer …
Im Rahmen dieses Digitalisierungskollegs sollen technologisch, ökonomisch, ökologisch und gesellschaftlich relevante Themengebiete für Studierende aus unterschiedlichen Fachrichtungen aufbereitet, …
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