Ofer Hadar

The arrangement of plant roots and their overall structure, known as root system architecture (RSA), plays an important role in acquiring water and nutrients essential for plant growth and development. Moreover, the RSA demonstrates remarkable adaptability to environmental stresses, making it a central factor in plant adaptation. Root traits, including root length, root diameter, root length density (RLD), and the presence of root hairs, play a crucial role in optimizing resource utilization within the soil and enhancing productivity. In particular, root hairs play a crucial role in the overall health and functioning of plants. These microscopic, hair-like structures extend from the surface of root cells and greatly increase the root’s surface area, which accounts for approximately 70% of the total root area. The characteristics of root hairs, such as their length and density, significantly enhance soil nutrients and water uptake. Considering these advantages, it is difficult to observe root hairs in a scene with low resolution. Therefore, we proposed a study using deep learning-based image super-resolution methods as a pre-processing step that helps to reconstruct finer details and structures within the root hairs, leading to a more accurate representation of their morphology, to understand the improvement in the response of root hairs under different environmental conditions and their impact on nutrient and water uptake, models need to be evolved.