Student Theses

Anomalieerkennung in Kleegras mittels maschinellem Lernen

Fusion of Distance and Hyperspectral Sensor Data for the Generation of Synthetic Plant Training Models

Agriculture today faces many economic and environmental challenges. Among other things, the increasing digitalisation of agriculture is seen as a basis for the further development of agriculture to meet these challenges. Artificial intelligence and machine learning can be seen as important core technologies. However, in order to train their algorithms, large amounts of training data are required, the data of which are usually difficult to collect under real conditions. Therefore, this work develops an open-source visual inspection system to capture and fuse geometric and spectral information under laboratory conditions is developed to generate synthetic plant training models. For this purpose, two laser-lineprofilers and a Pushbroom hyperspectral camera are combined in a benchtop experimental setup. The generated datasets of the recorded plant test objects are then algorithmically processed, merged into hyperspectral point clouds and exported as textured meshes for further use. Finally, the results of the plant samples and the developed visual inspection system are evaluated, discussed and validated. The evaluations show that the developed visual inspection system is more powerful than those of comparable scientific work.