UAV and ground-based imagery analysis detects canopy structure changes after canopy management applications

Jingyun Ouyang,Sigfredo Fuentes,Roberta De Bei,Cassandra Collins

doi:10.20870/oeno-one.2020.54.4.3647

Abstract

Aim: To analyse unmanned aerial vehicle (UAV)-based imagery to assess canopy structural changes after the application of different canopy management practices in the vineyard.Methods and results: Four different canopy management practices: i–ii) leaf removal within the bunch zone (eastern side/both eastern and western sides), iii) bunch thinning and iv) shoot trimming were applied to grapevines at veraison, in a commercial Cabernet-Sauvignon vineyard in McLaren Vale, South Australia. UAV-based imagery captures were taken: i) before the canopy treatments, ii) after the treatments and iii) at harvest to assess the treatment outcomes. Canopy volume, projected canopy area and normalized difference vegetation index (NDVI) were derived from the analysis of RGB and multispectral imagery collected using the UAV. Plant area index (PAI) was calculated using the smartphone app VitiCanopy as a ground-based measurement for comparison with UAV-derived measurements. Results showed that all three types of UAV-based measurements detected changes in the canopy structure after the application of canopy management practices, except for the bunch thinning treatment. As expected, ground-based PAI was the only technique to effectively detect internal canopy structure changes caused by bunch thinning. Canopy volume and PAI were found to better detect variations in canopy structure compared to NDVI and projected canopy area. The latter were negatively affected by the interference of the trimmed shoots left on the ground.Conclusions: UAV-based tools can provide accurate assessments to some canopy management outcomes at the vineyard scale. Among different UAV-based measurements, canopy volume was more sensitive to changes in canopy structure, compared to NDVI and projected canopy area, and demonstrated a greater potential to assess the outcomes of a range of canopy management practices. Significance and impact of the study: Canopy management practices are widely applied to regulate canopy growth, improve grape quality and reduce disease pressure in the bunch zone. Being able to detect major changes in canopy structure, with some limitations when the practice affects the internal structure (i.e., bunch thinning), UAV-based imagery analysis can be used to measure the outcome of common canopy management practices and it can improve the efficiency of vineyard management.

Highlights

Among the vineyard management practices, canopy management is widely applied to regulate canopy growth, reduce disease pressure, improve bud fertility and improve berry quality (Dry, 2008; Mirás-Avalos et al, 2017; Trought et al, 2017; Wolf et al, 2003)
Leaf removal and shoot trimming applied to one side of the canopy (LR-E and ST-E) had lowered canopy volume
The treatments used in this study showed the limitations of normalized difference vegetation index (NDVI) and canopy area measurements in detecting changes in canopy structure

Summary

Introduction

Among the vineyard management practices, canopy management is widely applied to regulate canopy growth, reduce disease pressure, improve bud fertility and improve berry quality (Dry, 2008; Mirás-Avalos et al, 2017; Trought et al, 2017; Wolf et al, 2003). The most accurate estimations for these parameters involve destructive and labour-intensive sampling practices in the field (Gower et al, 1999; Jonckheere et al, 2004) To overcome these disadvantages, there have been recent developments in smartphone apps that analyse upward-looking canopy cover imagery and offer an objective and accurate solution to the measurement of PAI (De Bei et al, 2016; Fuentes et al, 2014; Poblete-Echeverria et al, 2015). There have been recent developments in smartphone apps that analyse upward-looking canopy cover imagery and offer an objective and accurate solution to the measurement of PAI (De Bei et al, 2016; Fuentes et al, 2014; Poblete-Echeverria et al, 2015) These tools, that estimate PAI, have been effectively applied to assess changes in canopy structure during the growing season (De Bei et al, 2019; Wang et al, 2019)

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