Unsupervised Image Super-Resolution for Root Hair Enhancement and Improved Root Traits Measurements

Root hairs are essential for nutrient uptake and plant-microbe interactions, playing a vital role in plant health and agricultural productivity. They extend from the surface of root cells, significantly increasing the root surface area, and constitute roughly 70% of the total root area. Given these advantages, detecting root hairs in scenes with low resolution is challenging. Therefore, we have proposed a study that utilizes unsupervised image super-resolution methods to reconstruct finer details for root hairs using the dataset captured from our novel scanning camera known as RootCam. RootCam is a fully automated tool designed for monitoring and capturing plant root images for different vision tasks for a more accurate representation of their morphology and root trait measurements. Root hair super-resolution proves to be a powerful tool for root biology and its applications in precision agriculture. To the best of the authors' knowledge, this research study is the first that mainly focuses on root hairs and their trait measurement improvement using super-resolution. By visualizing the rhizosphere in high-resolution detail, we are able to notice a significant improvement in bell-pepper plant root hair count from 7 to 12, total root length from 0.32 to 1 mm, and root hair density (number of root hairs/mm) from 2.7 to 4.63, as upscaling factors rise from 2 to 8, respectively, when compared with bicubic and contrastive learning semi-supervised remote sensing image super (CLSR) for super-resolution. Researchers and farmers can make informed decisions about nutrient placement, irrigation management, and crop selection, optimizing resource use efficiency and crop yields.