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.
This study aimed to validate the use of UAV-based point cloud analysis to detect canopy decline severity and its spatial distribution in vineyards.A new approach to assess canopy decline, caused by Eutypa dieback-like symptoms, using unmanned aerial vehicle (UAV) remote sensing was compared with ground visual assessment in the vineyard. Canopy point cloud captured by UAV-based imagery during the growing season was analysed by a customized program to determine canopy decline severity and spatial distribution in a symptomatic Shiraz vineyard in Eden Valley, South Australia. Experienced assessors performed a ground visual assessment in the vineyard at E-L stage 15. k-means clustering was used to detect reduced canopy volume due to Eutypa dieback-like symptoms. Results from point cloud analysis showed that 12.5 % of the total canopy length in the vineyard had Eutypa dieback symptoms while the ground visual assessment detected 11.4 %. Confusion matrix results showed an accuracy of 87.4 % and a kappa coefficient of 0.43 compared with ground visual assessments. Additionally, automatic analysis of the point cloud was quicker than the ground visual assessment and generated precise geographic coordinates of the symptomatic canopy sections. Point cloud analysis can detect Eutypa dieback-like symptoms and its spatial distribution with 87.4 % accuracy, compared with the ground assessment. Similar to ground visual assessment, E-L stage 15 appears to be a suitable stage to apply point cloud analysis to make Eutypa dieback-like symptom assessments. Grapevine canopy decline, caused by various factors such as Eutypa dieback and inadequate management, can cause yield reduction and threaten vineyard longevity. Compared with tedious ground visual assessments, point cloud analysis can accelerate the assessment of canopy decline in vineyards and help with the planning of remedial practices using precise geographic coordinates of the affected sections.
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