Automated Image Analysis for Vineyard Yield Estimation
Summary
Automated image analysis has emerged as a transformative approach in viticulture, enabling rapid, non-invasive estimation of grape yield across diverse vineyard environments. By integrating remote sensing platforms—such as unmanned aerial vehicles (UAVs), ground vehicles and handheld devices—with advanced computer vision algorithms, researchers can detect and count grape clusters and individual berries, monitor growth stages and assess canopy structure. Techniques range from classical pixel-based segmentation to deep learning frameworks for object detection and instance segmentation. These methods deliver objective phenotypic data on cluster number, size and density, supporting precise yield forecasts, optimised resource allocation and improved harvest planning. The global significance of these technologies lies in their capacity to increase throughput, reduce labour costs and mitigate human error, thereby advancing precision viticulture practices and fostering sustainable vineyard management.
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Automated Image Analysis for Vineyard Yield Estimation publication trend
The graph below shows the total number of articles in automated image analysis for vineyard yield estimation across all publications each year (not limited to Nature Index journals).
Technical terms
UAV (Unmanned Aerial Vehicle): A remotely piloted aircraft used to acquire aerial imagery for vineyard monitoring.
Object detection: The process of localising and classifying discrete objects (e.g. grape bunches) within an image.
Instance segmentation: A vision task that delineates the precise pixel mask of each object instance, enabling individual berry or cluster counting.
F1-score: The harmonic mean of precision and recall, reflecting a model’s balance between false positives and false negatives.
Mean absolute error (MAE): The average of absolute differences between predicted and actual values, used to quantify yield estimation accuracy.
References
- Object detection and tracking on UAV RGB videos for early extraction of grape phenotypic traits. Computers and Electronics in Agriculture (2023).
- Automatic Bunch Detection in White Grape Varieties Using YOLOv3, YOLOv4, and YOLOv5 Deep Learning Algorithms. Agronomy (2022).
- Grapevine Yield and Leaf Area Estimation Using Supervised Classification Methodology on RGB Images Taken under Field Conditions. Sensors (2012).
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