An Object-Tracking Technique for Counting Grape Clusters in Brazilian Northeast's Pergola Vineyards

In precision viticulture, deep learning and computer vision have been increasingly employed to automate grape cluster counting, a crucial task for yield estimation and farm management. However, existing methods fail to address the unique challenges posed by the pergola vine training system - a widely used but understudied method, particularly in Brazil's Northeast, where viticulture plays a key role in economic and social development. To address these limitations, we propose a novel, lightweight approach that combines object detection (YOLOv8) and tracking (ByteTrack) to count grape clusters in handheld smartphone videos captured under diverse environmental conditions. Unlike previous works, our technique is applied to the unique layout of the pergola system, where occlusions, perspective variations, and irregular cluster distributions complicate automated counting. We evaluate our method on real-world vineyard footage, achieving $75.1 \% \text{mAP} {@} 50$ and a 69.9 % F1-score for cluster detection, with a final cluster count only 27 % above the actual value-establishing the first baseline for this challenging task. By enabling cost-effective, accessible yield estimation, our work contributes to improving efficiency and equity in viticulture. We publicly release our implementation to encourage further research and practical adoption (https://github.com/artsasse/pergola-grape-count/blob/main/grape_cluster_count.ipynb).