Precision agriculture utilizes Artificial Intelligence (AI) and remote sensing to address challenges in sustainable food production. A significant obstacle is effective weed management, particularly for species like black-grass (Alopecurus myosuroides), which visually resembles wheat. This thesis introduces AI-driven methods using Unmanned Aerial Vehicle (UAV)-based multispectral image analysis to improve weed detection, addressing key challenges such as limited datasets, sensor misalignment, and the need for robust algorithms.

The first part of the thesis addresses data scarcity using synthetic data augmentation. A study using a Mask Region-based Convolutional Neural Network (Mask R-CNN) model for black-grass detection shows that scaling foreground objects is a particularly effective technique for improving performance, providing a basis for adapting models to new field conditions with less data.

The second part focuses on multispectral data quality, where sensor misalignment can corrupt Vegetation Indices (VIs) like Normalized Difference Vegetation Index (NDVI). A method for sensor calibration and image registration is presented to correct for these errors. The study then evaluates various VIs, finding that models integrating indices like Triangular Greenness Index (TGI) and Excess Green Index (ExG) show improved detection performance compared to using only the basic spectral bands, particularly between crop rows.

Finally, the third part draws parallels to biometrics to explore the robustness of different network architectures. By evaluating triplet-based and Siamese networks for fingerprint recognition, it is demonstrated that clustering-based approaches using contrastive loss offer better performance with incomplete and variable data. This insight highlights broader principles for creating robust recognition systems.

Overall, this thesis contributes methods for addressing key obstacles in precision agriculture, spanning data augmentation, sensor calibration, and the optimization of deep learning models. The findings of this work aim to contribute to the ongoing development of more effective agricultural practices.