Automated Identification of Crop Tree Crowns from UAV Multispectral Imagery by Means of Morphological Image Analysis

Abstract

Within the context of precision agriculture, goods insurance, public subsidies, fire damage assessment, etc., accurate knowledge about the plant population in crops represents valuable information. In this regard, the use of Unmanned Aerial Vehicles (UAVs) has proliferated as an alternative to traditional plant counting methods, which are laborious, time demanding and prone to human error. Hence, a methodology for the automated detection, geolocation and counting of crop trees in intensive cultivation orchards from high resolution multispectral images, acquired by UAV-based aerial imaging, is proposed. After image acquisition, the captures are processed by means of photogrammetry to yield a 3D point cloud-based representation of the study plot. To exploit the elevation information contained in it and eventually identify the plants, the cloud is deterministically interpolated, and subsequently transformed into a greyscale image. This image is processed, by using mathematical morphology techniques, in such a way that the absolute height of the trees with respect to their local surroundings is exploited to segment the tree pixel-regions, by global statistical thresholding binarization. This approach makes the segmentation process robust against surfaces with elevation variations of any magnitude, or to possible distracting artefacts with heights lower than expected. Finally, the segmented image is analysed by means of an ad-hoc moment representation-based algorithm to estimate the location of the trees. The methodology was tested in an intensive olive orchard of 17.5 ha, with a population of 3919 trees. Because of the plot’s plant density and tree spacing pattern, typical of intensive plantations, many occurrences of intra-row tree aggregations were observed, increasing the complexity of the scenario under study. Notwithstanding, it was achieved a precision of 99.92%, a sensibility of 99.67% and an F-score of 99.75%, thus correctly identifying and geolocating 3906 plants. The generated 3D point cloud reported root-mean square errors (RMSE) in the X, Y and Z directions of 0.73 m, 0.39 m and 1.20 m, respectively. These results support the viability and robustness of this methodology as a phenotyping solution for the automated plant counting and geolocation in olive orchards.

A Critical Review of Unmanned Aerial Vehicles (UAVs) Use in Architecture and Urbanism: Scientometric and Bibliometric Analysis

“…(6), Vetrivel A. ( 5) and Roca D. (5). In the bibliographic coupling (Figure 6B), there are more connections between the main documents regardless of the field of application of UAVs in their research.…”

“…In recent years, researchers have shown great interest in the use of UAVs for civilian purposes such as search and rescue [2], remote sensing [3], construction and infrastructure inspection [4], agricultural precision [5], delivery of goods [6], real-time monitoring of road traffic [7], surveillance [8] and providing wireless coverage [9]. Their characteristics of multi-rotors such as hover, stability, manoeuvrability, vertical take-off and landing (VTOL) and low maintenance cost, make them the optimal system to carry out tasks that require low speeds, low heights and stable hover in the air such as taking photographs or taking measurements.…”

Tree Recognition on the Plantation Using UAV Images with Ultrahigh Spatial Resolution in a Complex Environment

“…order to eliminate the effects of weed interference, crown shape, and shooting height on the extraction of broad-leaved tree crowns, this study used spectral transformation, multi-radius filtering, and RF methods to enhance the recognition effect of ITCs in the test dataset. In the previous literature, the research on the recognition of ITCs has mainly focused on mature trees [17,18], many studies were undertaken in "clean" orchards [9,15,31,37,40], and little attention was paid to the detection of broad-leaved saplings, especially in the sites with complex forest conditions. As mentioned above, the four sample images in Figure A1 are typical scenes of manual screening, which means that in similar surveys, only a small amount of work is needed for intuitive manual selection, and a different number of sample images can be selected flexibly, so it is suitable for rapid survey inspection of forest plantation in an extensive area.…”

“…Another strength of MREFCM is in fusing the fragments of the same crown. In most agricultural plantations, the tree spacing is ideally regular [40], so the distance parameter can be used to determine the center of the crown and eliminate the interference of fragments. However, compared with plantations, in this study area, the tree spacing and the crown radii were quite different, so it was impossible to use the distance parameter to determine the crown center.…”

“…Computer-unified device architecture (CUDA) speeds up the calculation speed in the process of finding the largest canopy blob area [38], and light detection and ranging (LiDAR) point cloud analysis method also plays an important role [39]. Mathematical morphology also plays an important role, combined with elevation information to realize automatic counting and geographic positioning of olive crowns [40]. Multispectral images can accurately measure the structure of litchi trees [41].…”