@article{Deng2026, 
author = {Wenzhe Deng and Jiafei Zhang and Jing Huang and Hao Li and Zhiguo Han and Luyao Wang and Xueying Guan and Hongqing Ling and Tingting Wu and Weijuan Hu},
title = {HTPRootSlides: A high-throughput phenotyping platform for crop root germination dynamic screening},
year = {2026},
journal = {The Crop Journal},
volume = {14},
number = {2},
pages = {662-672},
keywords = {Root phenotyping, High-throughput imaging, Trait extraction, Seedling development, HTPRootSlides},
url = {https://www.sciopen.com/article/10.1016/j.cj.2025.10.014},
doi = {10.1016/j.cj.2025.10.014},
abstract = {Root phenotyping is crucial for advancing our understanding of plant development and adaptation. However, existing platforms often face challenges in balancing high-throughput capacity with long-term, high-frequency monitoring. To overcome this limitation, we present HTPRootSlides, an integrated root phenotyping platform designed for dynamic and scalable trait analysis. Its design features a circulating zone that accommodates 141 specialized root boxes for high-throughput operation synchronously. Root boxes follow a continuous S-shaped trajectory step by step, facilitating repetitive imaging for high-throughput, time-series data acquisition. To address challenges such as water vapor condensation and fine root entanglement, we developed a dedicated segmentation algorithm, achieving 89.56% accuracy in root isolation. Combining morphological and skeleton-based feature extraction techniques, the platform ensures comprehensive and efficient phenotypic trait quantification. We validated HTPRootSlides by dynamically monitoring root development in four staple crops (soybean, maize, wheat, and rice) during early-stage germination (< 14 d). The results demonstrate the capability of HTPRootSlides for high-frequency, high-precision and large-scale root phenotyping (< 1 h with 141 root boxes per run), offering researchers a powerful tool to investigate root dynamics and optimize crop performance through trait selection.}
}