This thesis contains a comprehensive analysis and discussion of issues and solutions encountered while developing a method for detecting roadside vegetation. The goal was to develop a method which would complement vehicles used for roadside maintenance. This would provide an opportunity to automate such tasks which are currently manually operated by maintenance workers. Vegetation detection is a common theme in the area of remote sensing where vegetation is detected from satellite images and used to monitor the health of vegetation which is affected by urbanization and industrialization. Most of vegetation detection methods are based on using satellite images which contain information from the spectrum that is invisible to the human eye. Our work focuses on developing a method based on using features from the visible part of the spectrum for detecting various vegetation types using a simple camera mounted on the vehicle. An extensive overview of developed methods is given starting with simpler approaches based on different manually selected features for machine learning and ending with a method based on deep learning where features are learned. We have shown that Fully Convolutional Neural Networks can be effectively used in a real world application for detecting roadside vegetation. For training and testing purposes, we have created our own image database which contains roadside vegetation in various conditions. Promising experimental results are presented with a discussion of encountered problems in real-world application as well as a comparison with several alternative approaches.