Faba bean crop plant identification using aerial multispectral imagery and convolutional neural network-based deep learning models

Faba bean (<i>Vicia faba L.</i>) is an important legume crop, offering benefits as a food source for humans and animals as well as a cover crop. For these reasons, it is envisioned to be a potential winter crop for the Mid-Atlantic region of the USA. To accelerate faba bean breeding programs in the Mid-Atlantic, traditional methods for phenotyping could be highly laborintensive and spatially subjective. Therefore, it is vital to develop high-throughput phenotyping methods for accurate and accelerated characterization of the faba bean crops. This study is aimed at developing an automated faba bean crop plant identification technique to aid in counting plant stands, critical for biomass and yield estimations. High resolution multispectral images were acquired using a small unoccupied aircraft system (SUAS) flown over one location (96) faba bean breeding trials from Oct through Dec 2024. Four convolutional neural network-based deep learning (DL) models, YOLOv10x, YOLO11x, YOLO12x, YOLO12n, were formulated, trained, and tuned for processing the acquired images to identify faba bean plants. The performance of the four models was evaluated at two epochs (50,100) and train-validation data split configurations (70:30, 80:20). YOLOv10x performed best at 50 epochs and 80:20 split while YOLO12x performed best at 100 epochs and 70:30 split. Across all models, performance improved with an increase from 70:30 to 80:20 split. Based on mAP50 at 100 epochs and 80:20 split, YOLO12n performed best, followed by YOLOv10x, YOLO11x and YOLO12x. YOLO12n showed the best and consistent accuracy for faba bean stand count estimation, showing potential for automated phenotyping of the crop.