Journal of Advances in Biology & Biotechnology

All plants rely on their roots for survival. Due to the natural plasticity of roots in response to different stimuli, breeders can investigate natural adaptation and uncover advantageous root features to increase plant yield in agricultural system. The plant's physiology, development, and ability to respond to different pressures are all influenced by the root system. Root system architecture (RSA)-related factors are very important for breeding selection. However, quantifying these traits is difficult, requires a lot of resources, and frequently produces a lot of variability. With the development of computer vision and machine learning (ML) technologies, which allow for effective trait extraction and evaluation, the use of RSA traits for genetic improvement to create more robust and resilient crop cultivars has attracted greater attention. Root phenotype is a crucial component of yield improvement which is regulated by the interaction of internal genetic factors and external environmental conditions. To meet the demands of population growth and climate change, significant increases in agricultural productivity are required. Enhancing crop root architecture has the potential to improve water and nutrient use efficiency; however, a major challenge remains in accurately characterizing the structure and function of the root phenome. Numerous advances have been made in recent years in the measurement and analysis of root system, including the development of 2D and 3D root phenotyping platforms. These platforms are high-throughput and non-invasive techniques for root phenotype characterization. These approaches involve the use of advanced imaging and analytical tools to collect data on root structure, growth, and function across a large number of plants, while enabling automated evaluation of multiple root traits. To phenotype root systems numerous imaging tools, software, and platforms have been developed. This study focuses on recent advancements in in-situ root phenotyping techniques that allow researchers and breeders to efficiently assess root characteristics and apply them to different breeding initiatives. In-situ root phenotyping techniques encompass a variety of 2D and 3D platforms for thorough and efficient root analysis. This review highlights current developments in in-situ root phenotyping and stresses their expanding potential to aid in the development of stress-resistant, high-yielding crops for sustainable agriculture.