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.

Extending the Aerial Image Analysis from the Detection of Tree Crowns In this study, we explore some possibilities of using aerial images captured by Unmanned Aerial Vehicles (UAV) and discuss the benefits of using them in the context of intelligent agriculture. A novel method that supports the detection and segmentation of tree crowns, the delineation of shadows, and which shows the direction of sunlight is presented. It uses simple observation strategies and commonly used digital image processing techniques such as visual color enhancement and perception, morphological operations, and segmentation based on a region growing method. The proposal is evaluated using a dataset with different types of crop areas and pasture lands. The results indicate that the proposal can effectively deal with the detection and segmentation of elements of interest in the scene, as well as the indication of the right side of the light source.