Eco-mind LK-1250 Rhizobox Imager. The LK-1250 rhizobox imaging system is designed for digital imaging of the transparent surfaces of individual rhizoboxes. The plant rhizobox imaging system uses a 20 MP CMOS sensor and LED with adjustable brightness. The resolution, area and distance of the camera equipment are set at the factory so that the system is ready to be used directly with A4 (letter) or custom sized rhizoboxes to obtain complete, clear and reflection-free root system images. A barcode scanner allows easy registration of individual rhizoboxes with the imaging software.
For a quote, please indicate the desired Rhizobox size; the standard size is A4/letter (outside dimensions) and/or get in contact.
Automated imaging of roots and the rhizosphere in ex situ rhizoboxes is emerging as a valuable tool in root research and in breeding crops that are better able to grow under stress conditions. By using automated imaging techniques, researchers can gain a better temporal resolution of root architecture development and root-soil interactions, and thus the dynamics of water & nutrient uptake - ultimately leading to the development of stress-tolerant crop varieties and a better understanding of root and rhizosphere functioning.
Traditionally, the study of roots and the rhizosphere has been challenging due to their complex and hidden nature below the soil surface. However, recent advances in imaging technologies and computer vision algorithms started to revolutionize root research. Rhizoboxes, controlled environments where plants can be grown while their roots are easily accessible for imaging, provide an ideal platform for studying root development and interactions with the surrounding soil. Automated imaging systems, permantantly installed next to these rhizoboxes ("RhizoPot Scanner Systems") or suited to rapidly image large numbers of individual Rhizoboxes, capture high-resolution images of roots and the rhizosphere at multiple time points, allowing researchers to track and analyze root growth patterns, root architecture, and responses to various stresses at unprecedented replication and frequency. One of the key advantages of automated imaging in Rhizoboxes is thus the ability to capture larger data sets at a high temporal resolution. Traditional, manual imaging methods are labor-intensive and time-consuming, limiting the scale and scope of Rhizobox studies. With automated imaging systems, researchers can collect a wealth of data on root development and turnover, allowing detailed analysis and identification of key traits associated with stress tolerance at suitable replication. By analyzing the captured images, researchers can quantify root length, surface area, branching patterns and root architecture parameters over time, which are important determinants of nutrient and water uptake efficiency. These data help breeders understand how different genotypes respond to stress and identify root traits that contribute to improved stress tolerance. Through selective breeding and genetic manipulation, crops with desirable root traits can be developed, leading to improved yields and resilience in challenging environments.
In conclusion, automated imaging of roots and rhizosphere in rhizoboxes, rhizonboxes offers significant advantages for root research and breeding of stress tolerant crops. It provides a non-invasive and high-throughput approach to study root growth dynamics, root architecture, and root-soil interactions ex situ. In addition, high-frequency imaging of the rhizosphere is helping to unravel the complex interactions between roots, soil, and microorganisms - particularly when applied in combination with other analyses (exudates, uptake rates, etc. ) in Rhizonboxes. Last, the more standardized acquisition of rhizobox images (similar colour and illumination schemes, hue etc.) is believed to facilitate the development / application of deep learning algorithms for automatic analysis of roots and rhizosphere traits.