Development of a Drone-Based Phenotyping System for European Pear Rust (Gymnosporangium sabinae) in Orchards

Abstract

Computer vision techniques offer promising tools for disease detection in orchards and can enable effective phenotyping for the selection of resistant cultivars in breeding programmes and research. In this study, a digital phenotyping system for disease detection and monitoring was developed using drones, object detection and photogrammetry, focusing on European pear rust (Gymnosporangium sabinae) as a model pathogen. High-resolution RGB images from ten low-altitude drone flights were collected in 2021, 2022 and 2023. A total of 16,251 annotations of leaves with pear rust symptoms were created on 584 images using the Computer Vision Annotation Tool (CVAT). The YOLO algorithm was used for the automatic detection of symptoms. A novel photogrammetric approach using Agisoft’s Metashape Professional software ensured the accurate localisation of symptoms. The geographic information system software QGIS calculated the infestation intensity per tree based on the canopy areas. This drone-based phenotyping system shows promising results and could considerably simplify the tasks involved in fruit breeding research.

Plant phenotyping relevance

ドローン画像、物体検出、写真測量を統合し、ナシ樹のさび病症状を検出・局在化して樹体ごとの感染強度を推定するデジタル表現型解析システムの開発が中心である。

Code and data availability

The paper's Data Availability Statement explicitly deposits the annotated UAV image dataset on Mendeley Data and the trained model, detection workflow, and Metashape loading script on figshare, both with public URLs matching allowed entries.