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Grape Cluster Detection Using UAV Photogrammetric Point Clouds as a Low-Cost Tool for Yield Forecasting in Vineyards
Jorge Torres-Sánchez1, Francisco Javier Mesas-Carrascosa2, Luis-Gonzaga Santesteban3
1Grupo Imaping, Instituto de Agricultura Sostenible-CSIC, 14004 Córdoba, Spain.
Sensors (Basel, Switzerland)
|April 30, 2021
Summary
This study introduces an automated method using drone imagery to detect grape clusters in red vineyards. This approach aids in accurate yield prediction and precision viticulture, overcoming limitations of traditional methods.
Area of Science:
- Agricultural Science
- Remote Sensing
- Computer Vision
Background:
- Traditional vineyard yield prediction is labor-intensive and struggles with spatial variability.
- Existing automated methods using ground imagery have limitations on uneven terrain.
- Unmanned Aerial Vehicle (UAV) photogrammetry shows promise for woody crop analysis.
Purpose of the Study:
- To develop an unsupervised, automated workflow for detecting grape clusters in red grapevines.
- To assess the impact of leaf removal on the accuracy of grape cluster detection.
- To enable accurate yield prediction and precision viticulture in vineyards.
Main Methods:
- Utilized UAV photogrammetric point clouds generated from imagery of commercial vineyards.
- Developed an automatic and unsupervised algorithm using free software for point cloud analysis.
- Investigated the influence of partial leaf removal on detection accuracy.
Main Results:
- Achieved R² values above 0.75 correlating harvest weight with projected grape cluster area.
- Reached an R² of 0.82 for vines with untouched full canopy in one dataset.
- Demonstrated the effectiveness of UAV photogrammetry for grape cluster detection, even with leaf occlusion.
Conclusions:
- The developed UAV-based method provides accurate grape cluster detection for yield prediction in red grape varieties.
- This technology facilitates the creation of yield estimation maps for precision viticulture.
- This represents the first application of UAV photogrammetric point clouds for grape cluster detection.
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