Recent advances in Artificial Intelligence (AI) offer a promising alternative to conventional drone-based remote sensing. In this study, we propose a sustainable stand-enumeration methodology for Moroccan sugar beet fields by integrating high-resolution imagery acquired with Unmanned Aerial Vehicles (UAVs) and a novel deep-learning pipeline. Four convolutional- and transformer-based architectures YOLOv5, Fast R-CNN, YOLOR (YOLO-R) and YOLOv7 were trained and evaluated on imagery from the Beni Mellal region using precision, recall, mean average precision (mAP) and plant survival-rate metrics. YOLOv5 achieved the best performance, with 97 % precision, 92 % recall and 96 % mAP, significantly outperforming the other models. These results demonstrate that AI-assisted UAV imagery can deliver highly accurate crop-stand counts, thereby supporting data-driven decision-making and enhancing sustainability in agricultural monitoring.