Wheat is a cornerstone of global food security, serving as a vital staple crop for billions of people worldwide. Accurate detection and counting of wheat kernels are crucial for monitoring crop development and forecasting yield. This chapter explores the application of object detection algorithms to identify wheat kernels using the Global Wheat Head Detection (GWHD) dataset. Four object detection models—Faster R-CNN, YOLOv5, YOLOv8, and the proposed YOLOv11—are evaluated across a series of experiments. Model performance is assessed using precision (P), recall (R), mean average precision at IoU 0.5 (mAP@50), and floating-point operations per second (FLOPs). The proposed YOLOv11 model demonstrates superior performance, achieving a precision of 99.5%, recall of 90%, and mAP@50 of 97%, while maintaining the lowest FLOPs among all models. In contrast, Faster R-CNN yields suboptimal results with 82% precision, 80% recall, and 84% mAP@50. YOLOv8 performs well, with 92.1% precision, 89.3% recall, and 94.4% mAP@50. These findings highlight YOLOv11 as the most efficient and accurate model for wheat kernel detection in this study.