Journal Article

Deep Learning–Based Detection and Quantification of Weed Seed Mixtures

Ahmed, Shahbaz, Savic, Marija, Revolinski, Samuel R., Maughan, P. Weston, Burke, Ian C., Kalin, Jessica

2024

Overview

Knowledge of weed seeds present in the soil seedbank is important for understanding population dynamics and forecasting future weed infestations. Quantification of the weed seedbank has historically been laborious, and few studies have attempted to quantify seedbanks on the scale required to make management decisions. An accurate, efficient, and ideally automated method to identify weed seeds in field samples is needed. To achieve sufficient precision, we leveraged YOLOv8, a machine learning object detection to accurately identify and count weed seeds obtained from the soil seedbank and weed seed collection. The YOLOv8 model, trained and evaluated using high-quality images captured with a digital microscope, achieved an overall accuracy and precision exceeding 80% confidence in distinguishing various weed seed species in both images and real-time videos. Despite the challenges associated with species having similar seed morphology, the application of YOLOv8 will facilitate rapid and accurate identification of weed seeds for the assessment of future weed management strategies.

• Publisher: The Weed Science Society of America,Cambridge University Press
• Subject: Accuracy / Artificial intelligence / Boxes / Datasets / Deep learning / Digital imaging / Identification / Identification methods / Image quality / Machine learning / mean average precision / object detection / Object recognition / Population dynamics / Population studies / RAPID COMMUNICATION / Real time / Seed banks / Seed collection / seedbank quantification / Seeds / Soil mixtures / Weed control / Weeds / YOLOv8