Abstract
Understanding plant root systems is critical for advancing research in soil-plant interactions, nutrient uptake, and overall plant health. However, accurate imaging of roots in subterranean environments remains a persistent challenge due to adverse conditions such as occlusion, varying soil moisture, and inherently low contrast, which limit the effectiveness of conventional vision-based approaches. In this work, we propose a novel underground imaging system that captures multiple overlapping views of plant roots and integrates a deep learning-based Multi-Image Super Resolution (MISR) framework designed to enhance root visibility and detail. To train and evaluate our approach, we construct a synthetic dataset that simulates realistic underground imaging scenarios, incorporating key environmental factors that affect image quality. Our proposed MISR algorithm leverages spatial redundancy across views to reconstruct high-resolution images with improved structural fidelity and visual clarity. Quantitative evaluations show that our approach outperforms state-of-the-art super resolution baselines, achieving a 2.3 percent reduction in BRISQUE, indicating improved image quality with the same CLIP-IQA score, thereby enabling enhanced phenotypic analysis of root systems. This, in turn, facilitates accurate estimation of critical root traits, including root hair count and root hair density. The proposed framework presents a promising direction for robust automatic underground plant root imaging and trait quantification for agricultural and ecological research.
Abstract (translated)
理解植物根系对于推进土壤-植物相互作用、养分吸收和整体植物健康的研究至关重要。然而，由于遮挡、土壤含水量变化以及低对比度等不利条件的影响，在地下环境中准确成像根系仍然是一项持续的挑战，这些因素限制了传统视觉方法的有效性。为此，我们提出了一种新颖的地下成像系统，该系统能够捕捉植物根系的多个重叠视图，并整合了一个基于深度学习的多图像超分辨率（MISR）框架，旨在提高根系的可见度和细节。为了训练并评估我们的方法，我们构建了一个合成数据集，模拟现实中的地下成像场景，并纳入影响图像质量的关键环境因素。 我们提出的MISR算法利用视图间的空间冗余来重建具有改进结构保真度和视觉清晰度的高分辨率图像。定量评价表明，与最先进的超分辨率基准相比，我们的方法在BRISQUE（一种用于测量图片感知质量问题的方法）指标上减少了2.3%，同时保持了相同的CLIP-IQA评分，从而提升了根系表型分析的质量。 这种方法使得关键根特性如根毛计数和密度的准确估计成为可能。所提出的框架为农业和生态研究中的鲁棒自动地下植物根系成像及性状量化提供了一个有前景的方向。
URL
https://arxiv.org/abs/2601.05482
