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.

翻译：理解植物根系对于推进土壤-植物相互作用、养分吸收及整体植物健康的研究至关重要。然而，由于遮挡、土壤湿度变化及固有的低对比度等不利条件，地下环境中根系的精确成像仍然是一个持续存在的挑战，这些条件限制了传统基于视觉方法的有效性。本研究提出了一种新颖的地下成像系统，该系统捕获植物根系的多幅重叠视图，并集成了一个基于深度学习的多图像超分辨率框架，旨在增强根系的可见性与细节。为了训练和评估我们的方法，我们构建了一个合成数据集，模拟真实的地下成像场景，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性，重建出具有更高结构保真度和视觉清晰度的高分辨率图像。定量评估表明，我们的方法优于最先进的超分辨率基线，实现了BRISQUE指标降低2.3%，在CLIP-IQA分数相同的情况下表明图像质量得到改善，从而能够对根系进行增强的表型分析。这进而有助于准确估计关键的根系性状，包括根毛数量和根毛密度。所提出的框架为农业和生态研究中鲁棒的自动地下植物根系成像与性状量化提供了一个有前景的方向。