1. Evaluating Deep Learning Models for Image-Based Corn Kernel Detection, Counting and Yield Prediction

Accurate estimation of kernel number in corn is essential for evaluating yield potential in breeding and agronomic research. However, manual kernel counting is labor-intensive, prone to human error, and impractical for large-scale datasets, while most existing automated devices are limited to simple counting tasks. This study evaluates deep learning-based approaches for automated kernel detection and counting using You Only Look Once models and Faster R-CNN. Specifically, YOLOv8x, YOLOv10x, and... B. Ghimire, L. Lacerda, T. Bourlai, G. Lu