An automated in-field transport and imaging chamber system for high-throughput phenotyping of potted soybean
Abstract Background In major soybean-growing regions worldwide, vertical (three-dimensional) planting systems are widely adopted. Achieving precise phenotyping of individual soybean plants is crucial for breeding shade-tolerant cultivars and optimizing high yields. However, canopy shading from talle...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-08-01
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| Series: | Plant Methods |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13007-025-01424-2 |
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| Summary: | Abstract Background In major soybean-growing regions worldwide, vertical (three-dimensional) planting systems are widely adopted. Achieving precise phenotyping of individual soybean plants is crucial for breeding shade-tolerant cultivars and optimizing high yields. However, canopy shading from taller crops severely restricts the acquisition of phenotypic information from the lower-growing soybeans, and conventional phenotyping platforms struggle to meet the demands of such complex planting structures. To address this challenge, this study developed a field-based high-throughput phenotyping platform specifically designed to accommodate the structural characteristics of vertical planting systems. Results The platform integrates the characteristics of vertical planting systems and consists of an imaging system and a rail-based transportation system.The imaging system balances the growth requirements of soybeans under natural conditions with the stability of indoor imaging, and is equipped with adjustable sensors, an automated rotating stage for image capture, and modules for image classification and storage. The transportation system includes X and Y dual-directional tracks and programmable rail carts, enabling automated movement of potted soybean plants in the field. Platform performance was validated through correlation analysis and predictive modeling. The extracted plant height and width showed high agreement with manual measurements, with coefficients of determination (R²) of 0.99 and 0.95, respectively. During the vegetative stage, the predictive accuracy (R²) for canopy fresh weight and leaf area reached 0.965 and 0.972, demonstrating strong predictive performance and robustness. In addition, the platform supports modular sensor integration and features an open-source control architecture, allowing seamless incorporation of additional sensors such as infrared cameras, LiDAR, and fluorescence imaging. This expands trait detection capacity while reducing costs for reuse and secondary development. Conclusion This study demonstrated the feasibility of combining natural field conditions with standardized indoor imaging for phenotypic research on soybeans under vertical planting systems. The platform provides a flexible and scalable technical solution for analyzing plant architecture and screening germplasm in complex planting environments, opening up new technological pathways for precision agriculture and crop breeding research. |
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| ISSN: | 1746-4811 |