In recent years, growers in the state are transitioning to new high yielding, pest and disease resistant cultivars. Such transition has created high demand for new tree fruit cultivars. Nursery growers have committed their incoming production of the next few years to meet such high demands. Though an opportunity, tree fruit nursery growers must grow and keep the pre-sold quantity of plants to supply the amount promised to the customers. Moreover, to keep the production economical amidst rising labor shortages, the nursery growers are looking at incorporating technological advances on the horizon. Also to insure the young nursery seedlings from adverse winter weather, growers need to accurately know the tree inventory grown in the actual field environment. Therefore, objective of this study was to develop and validate robust field grown apple nursery plant counting algorithm that is based only on elevation pixel values of small Unmanned Aerial System (UAS) based low altitude RGB imagery data. The nursery field images were obtained using small UAS operated at 30 m above the ground level. Image processing was performed in a Geographic Information System (GIS) software, where the pipeline was defined focusing on the isolation of apple plants based on thresholds of pixel height in circular regions along the crop line. In the first step the Digital Elevation Model (DEM) was processed in order to extract the Digital Terrain Model (DTM); the height of the plants was estimated according to the Crop Surface Model (CSM), which is the difference between the DEM and DTM. In the second step, the center lines of crop rows were extracted. As a third step, inside each row line generated were the points with a fixed spacing of 25 cm and buffered circular regions with a diameter of 50 cm. Those buffer areas were classified aiming following the logistic that “only the circles with maximum height higher than 23 cm can be counted as plants”. The proposed methodology presented satisfactory results, reaching an estimation with an accuracy of 95%.
