Research Article | Open Access

Volume 2026 |Article ID 100126 | https://doi.org/10.1016/j.plaphe.2025.100126

Aerial imagery and Segment Anything Model for architectural trait phenotyping to support genetic analysis in peanut breeding

Javier Rodriguez-Sanchez,^1,2 Raissa Martins Da Silva,³ Ye Chu,³ Lenin Rodriguez,³ Jing Zhang,⁴ Kyle Johnsen,¹ Peggy Ozias-Akins ,³ and Changying Li ⁵

¹School of Electrical and Computer Engineering, University of Georgia, Athens, GA, USA
²Department of Crop and Soil Sciences, University of Georgia, Tifton, GA, USA
³Department of Horticulture, University of Georgia, Tifton, GA, USA
⁴Department of Horticultural Science, North Carolina State University, Raleigh, NC, USA
⁵Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL, USA

Abstract

Unmanned aerial systems (UAS) are reliable tools for field phenotyping, enabling rapid, large-scale, and cost-effective data collection to support breeding programs. However, many UAS-based approaches rely on manual data processing, limiting scalability and efficiency. This study presents a fully automated pipeline for high-throughput phenotyping (HTP) of peanut crop architectural traits, including canopy height (CH), growth habit (GH), and mainstem prominence (MP) by integrating UAS imagery, a vision foundation model—Segment Anything Model (SAM), and convolutional neural networks (CNN). SAM auto-mask generator mode was used to identify field extent and orientation, while SAM interactive mode enabled individual plot segmentation using auto-generated point prompts. Terrain points automatically sampled near each plot were used to model the ground surface and compute the canopy height model, allowing CH estimations at the plot level. CH estimations showed strong agreement with manual measurements (R² = 0.78, RMSE = 3 cm, MAPE = 10 %). For MP and GH estimation, three pre-trained CNN models (AlexNet, ResNet18, and EfficientNet-B0) were evaluated, with AlexNet achieving the highest accuracy (89 % for GH, 83 % for MP). To assess the feasibility of using these HTP-derived estimations in plant breeding, quantitative trait loci (QTL) analysis was performed, identifying major-effect loci associated with these traits. The results were consistent with conventional QTL mapping methods, demonstrating that UAS-based phenotyping provides reliable trait data for genetic studies in peanut breeding. Overall, our deep learning-based data processing workflow minimizes manual efforts, providing an efficient and scalable approach that can accelerate genetic studies and trait selection in large-scale breeding programs.