Orchard Row Detection

The human population has grown rapidly in recent years, significantly increasing the need to optimize and safeguard food production and security. Subsequently, the agricultural industry is faced with the challenge of meeting the growing demand for food. To meet the projected food demands of 2050, agricultural production will need to rise by 70%. This work presents an automated approach to detect rows of trees or crops to allow farmers to quickly establish where there are issues with yield and fertility.

The main goal of our work is to identify underlying tree row structures using images obtained from an unmanned aerial vehicle (UAV). When identifying the rows, two main issues arise which increase the complexity of the problem. The first being that the orchard rows are planted in a combination of curved and straight-lines, with curved rows being particularly difficult to identify. The second problem being that the abundance of poorly classified trees leads to variations in tree size and positioning across the orchard.

There are several challenges presented by the orchard aerial inputs. These challenges vary across the different orchard inputs. In some cases, there is dense tree growth with overlapping trees. This results in erroneous detections that detect multiple trees as a single tree. In other cases, tree growth is sparse. When there are too few trees in a row, it violates the assumption that inter-row distances are smaller than intra-row distances, thus making the detection of rows very difficult. Unfortunately, tree height information and contour information were not consistently captured for all of the orchard inputs and was therefore not used in any of the implemented approaches.