Assessment of canopy size using UAV-based point cloud analysis to detect the severity and spatial distribution of canopy decline

Abstract

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.

Keywords

• canopy assessments
• point cloud analysis
• spatial variation
• grapevine trunk disease