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Abstract
Accurate and rapid measurement of tall fescue abundance is essential for efficient forage management and mitigating livestock exposure to potential endophyte-related toxicity. Traditional visual estimation methods are labor-intensive and are subject to change depending on the observer doing the calculations, which limits their scalability and consistency. This study explores the application of deep learning techniques to automate and improve the classification of tall fescue abundance across pasture systems in Kentucky. Ground-truth abundance classes were evaluated using one of the traditional methods with occupancy grid and compared against estimates derived from red-green-blue images. Two convolutional neural network architectures, YouOnlyLookOnce (YOLO) and ResNet were employed for image-based classification. Among the models tested, YOLOv8x achieved a classification accuracy of 96% across three abundance classes, effectively distinguishing tall fescue from other vegetation and bare soil. Results demonstrated the potential of artificial intelligence in detecting spatial distribution of tall fescue, thereby supporting risk assessment of toxicity. Compared to the traditional methods, the deep learning approach improves efficiency and reduces manual labor efforts while maintaining highly accurate prediction performance for continuous monitoring applications.
Document Type
Article
Publication Date
2026
Digital Object Identifier (DOI)
https://doi.org/10.3389/fagro.2026.1749362
Funding Information
The author(s) declared that financial support was received for this work and/or its publication. The USDA Agricultural Research Service (ARS) provided funding through a Non−Assistance Cooperative Agreement (NACA) supported field data collection. The University of Kentucky Office of the Provost’s 2024 Impact Award for the “UK Artificial Intelligence and Machine Learning Hub” provided funding to hire student researchers who contributed to data processing and analysis. Additional support was provided by U.S. Department of Defense Smart Scholarship for the author (RB) and Hatch Multistate, project accession no. 7008110 and 7008088 for the authors (KM and SR), respectively, from the USDA National Institute of Food and Agriculture.
Repository Citation
Binkley, Rose E.; Revolinski, Samuel R.; Gotsick, Echo Elizabeth; Smith, Ray; Wang, Zeya; and Mizuta, Katsutoshi, "Deep learning-based quantification of tall fescue abundance in pastures" (2026). Plant and Soil Sciences Faculty Publications. 186.
https://uknowledge.uky.edu/pss_facpub/186
Notes/Citation Information
© 2026 Binkley, Revolinski, Gotsick, Smith, Wang and Mizuta. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.