Missing Plant Detection in Vineyards Using UAV Angled RGB Imagery Acquired in Dormant Period

Abstract

Since 2010, more and more farmers have been using remote sensing data from unmanned aerial vehicles, which have a high spatial–temporal resolution, to determine the status of their crops and how their fields change. Imaging sensors, such as multispectral and RGB cameras, are the most widely used tool in vineyards to characterize the vegetative development of the canopy and detect the presence of missing vines along the rows. In this study, the authors propose different approaches to identify and locate each vine within a commercial vineyard using angled RGB images acquired during winter in the dormant period (without canopy leaves), thus minimizing any disturbance to the agronomic practices commonly conducted in the vegetative period. Using a combination of photogrammetric techniques and spatial analysis tools, a workflow was developed to extract each post and vine trunk from a dense point cloud and then assess the number and position of missing vines with high precision. In order to correctly identify the vines and missing vines, the performance of four methods was evaluated, and the best performing one achieved 95.10% precision and 92.72% overall accuracy. The results confirm that the methodology developed represents an effective support in the decision-making processes for the correct management of missing vines, which is essential for preserving a vineyard’s productive capacity and, more importantly, to ensure the farmer’s economic return.

Plant phenotyping relevance

UAV画像とフォトグラメトリを用いてブドウ樹の位置・欠株を抽出する手法を開発し、複数手法の性能評価も行っており、植物状態の測定法が研究の中心である。

Code and data availability

The paper's UAV RGB imagery, point cloud slices, and QGIS workflow outputs are not publicly deposited; the Data Availability Statement says they are available from the corresponding author upon reasonable request. No public code or dataset URL is provided.

No evidence-backed public reproduction asset is currently recorded.