Crop edge detection based on stereo vision This paper proposes a robust detection method of uncut crop edges which is used for automated guidance of a combine harvester. The utilized stereo vision system allows for realtime depth perception of the environment. A threedimensional elevation map of the terrain is constructed by the point cloud acquired in this way. The heights of crop and harvested areas are estimated using Expectation Maximization and segmented using of the clustering results. In a rowwise processing step, each scan line of heights is crosscorrelated with a model function to compute possible candidate points located at the very crop edge. Using robust linear regression, a linear crop edge model is calculated, modeling the spatial distribution of the candidate points. An overall crop edge model is updated via exponentially weighted moving average.

Trunk Detection and Tree Disparity Calculation in Uncontrolled Environments Computer vision is an area have proven to play an essential role in urban and rural applications like medical, agriculture, and remote sensing. The use of image processing methods for simulating the visual capability of robots plays a crucial role in the consolidation of smart farming. The understanding of the complexity of outdoor environments, where the robot performs its task, is an essential issue for the development of efficient processes of autonomous mobility, especially in areas with uneven illumination, unpredictable weather conditions, and different color shades. In this study, we present a new method to detect and segment tree trunks from unstructured environments where natural properties such as lighting and terrain shape form a variety of noncontrolled conditions. We prepared a dataset with stereo image pairs and ground truth maps to calculate disparities and to evaluate the proposed method in the application of smart farming. The results show that the presented approach can segment trees with high precision, which is an important step in calculating the disparity of external components by systems that use the stereoscopic view.