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

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.

Automated summary

Since 2010, farmers have increasingly used remote sensing data from unmanned aerial vehicles to monitor crop status and field changes. This study proposes different approaches to identify and locate each vine within a vineyard using angled RGB images, allowing for precise assessment of missing vines. The results confirm the effectiveness of this methodology in decision-making processes for managing missing vines, ensuring the vineyard's productivity and the farmer's economic return.