Using 3D Point Clouds Derived from UAV RGB Imagery to Describe Vineyard 3D Macro-Structure

Marie Weiss,Frédéric Baret

doi:10.3390/rs9020111

Abstract

In the context of precision viticulture, remote sensing in the optical domain offers a potential way to map crop structure characteristics, such as vegetation cover fraction, row orientation or leaf area index, that are later used in decision support tools. A method based on the RGB color model imagery acquired with an unmanned aerial vehicle (UAV) is proposed to describe the vineyard 3D macro-structure. The dense point cloud is first extracted from the overlapping RGB images acquired over the vineyard using the Structure from Motion algorithm implemented in the Agisoft PhotoScan software. Then, the terrain altitude extracted from the dense point cloud is used to get the 2D distribution of height of the vineyard. By applying a threshold on the height, the rows are separated from the row spacing. Row height, width and spacing are then estimated as well as the vineyard cover fraction and the percentage of missing segments along the rows. Results are compared with ground measurements with root mean square error (RMSE) = 9.8 cm for row height, RMSE = 8.7 cm for row width and RMSE = 7 cm for row spacing. The row width, cover fraction, as well as the percentage of missing row segments, appear to be sensitive to the quality of the dense point cloud. Optimal flight configuration and camera setting are therefore mandatory to access these characteristics with a good accuracy.

Highlights

Grapevine productivity depends on physical, biological and chemical factors, including climate, soil characteristics, grape variety, topography, occurrence of pests or diseases and management practices
The objective of this study is to develop an algorithm for vineyard structural characteristic estimation from dense point clouds derived from the RGB color model images acquired with an unmanned aerial vehicle (UAV)
A dedicated method was developed to estimate several vineyard characteristics using the RGB method imagery acquired from an unmanned aerial vehicle (UAV) platform

Summary

Introduction

Grapevine productivity depends on physical, biological and chemical factors, including climate, soil characteristics, grape variety, topography, occurrence of pests or diseases and management practices. An optimal management of cultural practices during the growing season and at harvesting must be taken into account In this context of precision viticulture, which began in 2000 as reviewed by [1], remote sensing in the optical domain offers a potential way to extract spatial information on the crop state in a non-destructive way. It can later be used in decision support tools. This threshold, the threshold, pixel is considered value is assign used assign pixel to aBelow background Determine theTo optimal heightthe threshold create threshold the binary to image, thethe height cumulative frequency as determine optimaltoheight create binary image, the height distributionfrequency in each ESU is analyzed.

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