Monitoring Vineyard Canopy Management Operations Using Uav-Acquired Photogrammetric Point Clouds
subject: 3D mapping, change detection, leaf removal, remote sensing, shoot thinning, shoot trimming
description: Canopy management operations, such as shoot thinning, leaf removal, and shoot trimming, are among the most relevant agricultural practices in viticulture. However, the supervision of these tasks demands a visual inspection of the whole vineyard, which is time-consuming and laborious. The application of photogrammetric techniques to images acquired with an Unmanned Aerial Vehicle (UAV) has proved to be an efficient way to measure woody crops canopy. Consequently, the objective of this work was to determine whether the use of UAV photogrammetry allows the detection of canopy management operations. A UAV equipped with an RGB digital camera was used to acquire images with high overlap over different canopy management experiments in four vineyards with the aim of characterizing vine dimensions before and after shoot thinning, leaf removal, and shoot trimming operations. The images were processed to generate photogrammetric point clouds of every vine that were analyzed using a fully automated object-based image analysis algorithm. Two approaches were tested in the analysis of the UAV derived data: 1) to determine whether the comparison of the vine dimensions before and after the treatments allowed the detection of the canopy management operations; and 2) to study the vine dimensions after the operations and assess the possibility of detecting these operations using only the data from the flight after them. The first approach successfully detected the canopy management. Regarding the second approach, significant differences in the vine dimensions after the treatments were detected in all the experiments, and the vines under the shoot trimming treatment could be easily and accurately detected based on a fixed threshold.
creator: Francisca López-Granados, Jorge Torres-Sánchez, Francisco M. Jiménez-Brenes, Oihane Oneka, Diana Marín, Maite Loidi, Ana I. de Castro, L.G. Santesteban
source: Remote Sensing
publisher: MDPI AG
date: 2020
relation: https://www.mdpi.com/2072-4292/12/14/2331
coverage: Science