Precise and accurate quantification of blackberry flowers are essential for yield forecasting, phenotypic assessment, and enhancing management techniques in precision agriculture. Threshold segmentation of images for blackberry feature quantification could be challenging due to shadows and background variability, while manual flower estimation is subjective and time-consuming. The objective of this research was to apply artificial intelligence and computer vision to identify and quantify blackberry flowers from Unmanned Aerial Vehicle (UAV) remote sensing. A computer vision algorithm You Only Look Once (YOLO) was trained with 1142 image datasets of blackberry flowers to develop an image processing workflow to quantify UAV captured images. A performance analysis was conducted with YOLO variants (YOLOv8s–YOLO12s) for quantifying blackberry flowers. YOLOv10s achieved the best performance with a mAP@0.5 of 58%, precision of 60%, and recall of 58%. Input resolution had a notable impact, performing better at 1024×1024 pixels (mAP@0.5 = 55%) than at 640×640 (mAP@0.5 = 30%). Increasing the training dataset from 250 to 1,142 images progressively improved detection accuracy, highlighting the value of data volume for model generalization. Additionally, flower counts predicted by YOLOv10s showed a strong correlation with flower-to-vegetation ratio (FVR; r = 0.71, p < .001), supporting FVR as a practical proxy for estimating floral density in the field. A contribution to computer assisted agriculture integration in the blackberry industry has been made by investigating the performance of computer vision algorithms on blackberry flower detection. Challenges such as small and uneven sized flowers, overlapping occlusion, and plot-wise analysis must be further investigated. Keywords. UAV, automation, blackberry, phenotyping, artificial intelligence.
