Developing a segment anything model-based framework for automated plot extraction

Abstract

Purpose

Automated plot extraction in agronomic research field trials is essential for high-throughput phenotyping and precision agriculture. Accurate delineation of plot boundaries enables reliable crop type classification, yield estimation, and crop health monitoring. However, traditional plot extraction methods rely heavily on manual digitization, which is time-consuming, labor-intensive, and prone to inconsistencies. This study aims to develop a Segment Anything Model (SAM)-based framework that automates plot extraction while maintaining high accuracy across diverse agricultural field conditions.

Methods

The proposed framework consists of mask generation, plot orientation estimation, and plot refinement. SAM is leveraged to generate plot masks, which are subsequently filtered and refined to ensure precise boundary delineation. The method is designed to function without the need for model training or fine-tuning, making it highly adaptable across different datasets.

Results

The framework was validated on five datasets, demonstrating robust performance under varying field conditions. The pixel-based evaluation yielded an average F1 score of 89.54%. For polygon-based evaluation, the framework achieved 99.71% precision at IoU=50% and an average precision of 68.51% across IoU thresholds from 50 to 95%, confirming its ability to accurately extract plot boundaries. A Canopeo-based regression analysis further demonstrated that the extracted plots provide more reliable phenotypic estimates compared to manually digitized ground reference data.

Conclusions

The proposed framework significantly reduces manual effort while ensuring high precision and scalability for large-scale phenotyping applications. By relying solely on RGB imagery and zero-shot segmentation, it enhances accessibility for real-world agricultural research. Future work will focus on extending the framework to irregular plot structures, diverse crop types, and computational optimizations for large-scale implementation.