Automatic Grapevine Trunk Detection on UAV-Based Point Cloud

Abstract

The optimisation of vineyards management requires efficient and automated methods able to identify individual plants. In the last few years, Unmanned Aerial Vehicles (UAVs) have become one of the main sources of remote sensing information for Precision Viticulture (PV) applications. In fact, high resolution UAV-based imagery offers a unique capability for modelling plant’s structure making possible the recognition of significant geometrical features in photogrammetric point clouds. Despite the proliferation of innovative technologies in viticulture, the identification of individual grapevines relies on image-based segmentation techniques. In that way, grapevine and non-grapevine features are separated and individual plants are estimated usually considering a fixed distance between them. In this study, an automatic method for grapevine trunk detection, using 3D point cloud data, is presented. The proposed method focuses on the recognition of key geometrical parameters to ensure the existence of every plant in the 3D model. The method was tested in different commercial vineyards and to push it to its limit a vineyard characterised by several missing plants along the vine rows, irregular distances between plants and occluded trunks by dense vegetation in some areas, was also used. The proposed method represents a disruption in relation to the state of the art, and is able to identify individual trunks, posts and missing plants based on the interpretation and analysis of a 3D point cloud. Moreover, a validation process was carried out allowing concluding that the method has a high performance, especially when it is applied to 3D point clouds generated in phases in which the leaves are not yet very dense (January to May). However, if correct flight parametrizations are set, the method remains effective throughout the entire vegetative cycle.

Grape Cluster Detection Using UAV Photogrammetric Point Clouds as a Low-Cost Tool for Yield Forecasting in Vineyards

“…This fact makes possible the estimation of harvest weight from all the vines in the parcel at the right moment using a nondestructive method without limiting the yield forecasting to some sampling points, which would hinder the detection of the spatial variability of the vineyard production. Since the points classified as grapes have coordinates, the methodology presented allows the generation of yield maps if it is combined with procedures for individual vine detection or division of vine rows in segments like the ones developed by [ 38 ] and [ 39 ], respectively. The harvest estimation maps generated near the harvest time allow the zoning of harvest operations, reserving the most adequate yield levels, e.g., for premium wine production, as suggested by Ballesteros et al [ 25 ].…”

“…Since the points classified as grapes have coordinates, the methodology presented allows the generation of yield maps if it is combined with procedures for individual vine detection or division of vine rows in segments like the ones developed by [38] and [39], respectively. The harvest estimation maps generated near the harvest time allow the zoning of harvest operations, reserving the most adequate yield levels, e.g., for premium wine production, as suggested by Ballesteros et al [25].…”

Canopy Volume Extraction of Citrus reticulate Blanco cv. Shatangju Trees Using UAV Image-Based Point Cloud Deep Learning

“…With the development of UAV technology, UAV images have also been widely used [49][50][51][52][53][54][55][56]. Studies [10][11][12][13] have shown that UAV tilt photogrammetry can quickly and conveniently obtain images and texture information of forests, pine trees, eucalyptus, etc. UAV tilt photogrammetry is also suitable to obtain the image of the Citrus reticulate Blanco cv.…”

“…Gülci [12] established a canopy height model with UAV remote sensing technology to estimate the number, height and canopy coverage of pine trees. On the basis of UAV tilt photogrammetry images and point cloud reconstruction, Jurado et al [13] developed an automatic detection method of grapevine trunk using 3D point cloud data with good robustness. Camarretta et al [14] used UAV-LiDAR for rapid phenotyping of eucalyptus trees to study inter-species differences in productivity and differences in key features of tree structure.…”

Využitie UAV snímok na poloautomatickú detekciu výpadkov viniča vo vinohradoch Jelenca a Topoľčianok (Slovensko)

“…____________________ DOI: https://doi.org/10.33542/GC2020-2-07 Zo snímok, ktoré zachytávajú rady viniča, sa dá ľahko detegovať priebeh radu a šírka radu (Wasser et al 2015, Primicerio et al 2017. Následná obrazová analýza snímok vinohradu vie poskytnúť rýchly a nedeštruktívny spôsob zachytenia presných informácií o množstve (Reis et al 2012) a kvalite viniča, chorobách (Kerkech 2018), odhade výnosu (Colaço et al 2020), množstve koreňov a výpadkov koreňov (Whalley 2013, Jurado 2020. Za výpadok môžeme považovať nesúvislý zápoj viniča v rade.…”