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RhizoPot platform: A high-throughput in situ root phenotyping platform with integrated hardware and software
Hongjuan Zhao1, Nan Wang1,2, Hongchun Sun1
1State Key Laboratory of North China Crop Improvement and Regulation/Key Laboratory of Crop Growth Regulation of Hebei Province/College of Agronomy, Hebei Agricultural University, Baoding, China.
Frontiers in Plant Science
|October 17, 2022
Summary
The RhizoPot platform enables high-throughput, in situ root phenotyping for quantitative analysis. This cost-effective system automates image acquisition and analysis, improving the study of root development and stress resistance.
Area of Science:
- Plant science
- Agricultural engineering
- Computational biology
Background:
- Quantitative analysis of root development is crucial for understanding plant responses to environmental factors, particularly abiotic stresses.
- Non-destructive root phenotyping, such as using rhizotrons, enhances data acquisition but faces challenges in high-throughput imaging and analysis.
- Developing efficient equipment for in situ root image acquisition and analysis remains a significant hurdle in plant science.
Purpose of the Study:
- To propose and evaluate the RhizoPot platform, a novel high-throughput system for in situ root phenotyping.
- To integrate plant culture, automatic image acquisition, and image segmentation for quantitative root analysis.
- To demonstrate the platform's capability in monitoring dynamic root phenotypes and analyzing root morphology, growth rate, and lifespan.
Main Methods:
- The RhizoPot platform integrates plant cultivation with automated, non-destructive in situ root image acquisition using RhizoAuto software (50-4800 dpi).
- An improved DeepLabv3+ tool was employed for automated root segmentation and extraction from high-resolution images (>1200 dpi).
- Quantitative analysis of root morphology, growth rate, and lifespan was performed using conventional software (WinRhizo, RhizoVision Explorer) on segmented images.
Main Results:
- The RhizoPot platform successfully enabled high-throughput, automated, and non-destructive imaging of plant roots in situ.
- Automated image segmentation using DeepLabv3+ facilitated detailed analysis of root morphology and growth dynamics.
- The platform effectively illustrated dynamic root phenotype responses in cotton, providing quantitative insights.
Conclusions:
- The RhizoPot platform offers a low-cost, high-efficiency, and high-throughput solution for in situ root monitoring.
- This system significantly advances the quantitative analysis of root development and phenotypes.
- The platform is a valuable tool for research on plant adaptation to abiotic stresses and for improving crop resilience.
