发表机构
University of Illinois at Urbana-Champaign; Center for Advanced Bioenergy and Bioproducts Innovation（伊利诺伊大学厄巴纳-香槟分校; 先进生物能源与生物制品创新中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
RootQuantV2利用自监督ViT替换CNN骨干，通过参数高效微调实现从微根管图像直接回归根系性状，以少量参数训练达到高精度，显著降低误差，实现高通量自动化估计。
AI 中文摘要
田间作物根系性状的高通量表型分析解决方案的缺乏，严重限制了对地下性状和过程的理解与改良。微根管是田间环境中标准的非破坏性根系表型分析方法。需要计算机视觉解决方案以实现规模化的自动化性状估计，但训练数据稀缺，且人工标注通常难以获取，因为它们存在于专有软件中，该软件仅导出每张图像的根长和根表面积标量总数。尽管如此，大量这些根系性状的数值档案已经存在。RootQuant表明，可以通过回归直接从整张图像预测性状，从而从流程中移除人工描绘的掩膜；RootQuantV2通过用自监督ViT替换RootQuant的CNN骨干网络，进一步推进了这一思路。我们采用混合参数高效方案适配冻结的DINOv3 ViT-L/16。仅训练1190万参数（占模型的3.78%），RootQuantV2的长度和面积R²分别达到0.950和0.930，同时与RootQuant相比，长度/面积RMSE降低了24.3%/20.7%。因此，RootQuantV2将遗留的数值档案重新用于高通量、自动化的根系性状估计。
英文摘要
A lack of high-throughput phenotyping solutions for root traits in field-grown crops has severely constrained understanding and improvement of below-ground traits and processes. Minirhizotrons are the standard non-destructive root-phenotyping method in field environments. Computer vision solutions are needed to allow automated trait estimation at scale, but training data is scarce and human annotations are often inaccessible because they reside in proprietary software that only exports per-image scalar totals of root length and surface area. Nevertheless, large numeric archives of these root traits already exist. RootQuant showed that the traits can be predicted directly from the whole image by regression, thus removing manually traced masks from the pipeline; RootQuantV2 takes that idea further by replacing RootQuant's CNN backbone with a self-supervised ViT. We adapt a frozen DINOv3 ViT-L/16 with a hybrid parameter-efficient scheme. Training only 11.9M parameters (3.78% of the model), RootQuantV2 achieves length and area $R^2$ of 0.950 and 0.930, respectively, while lowering length/area RMSE by 24.3%/20.7% over RootQuant. RootQuantV2 thus repurposes legacy numeric archives for high-throughput, automated root trait estimation.
Comments20 pages (15 main + 5 references), 4 figures, 5 tables. Accepted to the Computer Vision in Plant Phenotyping and Agriculture (CVPPA) Workshop at ECCV 2026. Code and weights: https://github.com/leakey-lab/RootQuantV2
