Convolutional Neural Networks for Detecting White Grape Clusters in High-Density Vineyards

This study addresses the challenge of detecting white grape clusters (Vitis vinifera L) in high-density vineyard canopies, a critical task for precision viticulture and yield es-timation. Traditional statistical and image-processing methods have struggled with occlusion issues. In this work, over 100 field RGB images were collected at La Bergonza (Toledo, Spain) and expanded through data augmentation, with various preprocessing strategies tested to enhance cluster visibility. Convolutional Neural Network (CNN) architectures were compared, highlighting YOLOv8 as superior to Mask R-CNN in both accuracy and efficiency. YOLOv8, trained for up to 100 epochs on equalized and augmented datasets, achieved outstanding performance: 84.9% precision, 72.6% recall, and mAP@0.5 of 83%, far surpassing Mask R-CNN (17% precision, 26% recall). The model successfully detected partially hidden clusters, including those invisible to hu-man experts, better than previous studies that required controlled backgrounds or ar-tificial lighting. Results confirm that combining RGB equalization with data augmen-tation optimizes detection. These findings underscore the potential of deep learning and low-cost RGB imaging systems to enable automated, scalable solutions for yield estimation and canopy analysis. In conclusion, YOLOv8 emerges as a promising tool for accurate grape bunch detection under field conditions, overcoming previous limi-tations.