Recently, technical engineers from Eco-mind traveled to the Institute of Subtropical Agriculture, Chinese Academy of Sciences, to complete on-site operation training for the LK-1580 MultiRhizo Composite Root Growth Dynamics Monitoring System. The system will be used for dynamic research on rice root growth.
Rice roots remain in a water-saturated environment for extended periods. Traditional root-washing and sampling methods are destructive, time-consuming, and low in temporal resolution, making it difficult to support continuous observation of fine root turnover, root elongation, and senescence. Based on minirhizotron technology, the LK-1580 MultiRhizo enables in-situ tracking of dynamic changes at the same root location without destroying the soil structure — precisely meeting the demands of "in-situ, non-destructive, and long-term" root research.
Why Choose the LK-1580 MultiRhizo
Minirhizotron photography offers high resolution but covers only a small area per image, while scanning captures a large area but with less detail. The LK-1580 MultiRhizo integrates photographic and scanning imaging into one system:
Photo Mode: High-resolution local imaging to observe fine roots, root tips, and root hairs.
Scan Mode: Rapid large-area imaging to capture overall root distribution.
Integrated Control Software: Flexible switching between detail and overview perspectives.
iRoot-2024 Intelligent Root Analysis Software: Based on U-Net convolutional neural networks and image semantic segmentation, it supports batch automatic identification of minirhizotron images, outputting total root length, total surface area, total projected area, average diameter, total volume, total root tip number, and parameters by diameter class.
The system can also generate probability maps, binarized images, grading maps, root tip distribution maps, and skeleton maps, assisting researchers in intuitively assessing root recognition results and root system structure. Compared with traditional manual root tracing, it is particularly suitable for analyzing rice root images in long-term fixed-point observation projects.