Implementation of an SfM-MVS-based photogrammetry approach for detailed 3D reconstruction of plants
Status PubMed-not-MEDLINE Jazyk angličtina Země Anglie, Velká Británie Médium electronic
Typ dokumentu časopisecké články
Grantová podpora
A1_FPBT_2025_005
Internal Grant Agency of the University of Chemistry and Technology in Prague
PubMed
41068873
PubMed Central
PMC12512472
DOI
10.1186/s13007-025-01445-x
PII: 10.1186/s13007-025-01445-x
Knihovny.cz E-zdroje
- Klíčová slova
- 3D reconstruction, Close-range photogrammetry, Morphological traits, Plant phenotyping, Precision agriculture, SfM-MVS-based data processing,
- Publikační typ
- časopisecké články MeSH
In recent years, non-destructive and non-invasive methods for 3D plant reconstruction have gained increasing importance in plant phenotyping. Morphological traits reflect the physiological status of a plant and serve as key indicators for precision agriculture, crop protection, and food quality assessment. Accurate and efficient 3D modelling enables objective and repeatable monitoring of plant development and health, thus supporting data-driven decision-making in agricultural and food research. This study presents a novel, cost-effective, and flexible photogrammetric apparatus for the routine analysis of plant morphological traits under controlled laboratory conditions. Existing systems often rely on expensive instrumentation and provide limited adaptability, whereas the platform described here combines affordability with high precision and robustness. A key innovation is the use of a robotic arm to control an industrial RGB camera, providing substantial flexibility in image acquisition. This mobility ensures comprehensive coverage of plants of different sizes and architectures while minimising occlusions. Another distinctive feature is the implementation of an optimised parameter tweak in the photogrammetric pipeline, which markedly improves the reconstruction of thin and delicate plant parts such as leaves, petioles, and fine stems. In combination with optimised acquisition parameters, including an exposure time of 50 milliseconds, a tweak value of 0.9, and a camera-to-object distance of 16 centimetres, the system achieves consistent model fidelity across diverse plant structures. Efficiency was further enhanced through automation and an optimised scanning procedure. Comparative testing showed that using a larger number of camera positions with fewer frames per position improved throughput, with the best configuration consisting of three height levels and 40 frames each. These improvements reduced the processing time by 75%, decreasing the average scan duration from 8 min to only 2.7 min per plant, while maintaining accuracy and reliability. Overall, the developed apparatus constitutes a reliable and low-cost solution that integrates robotic-assisted flexibility, improved reconstruction through the parameter tweak, and markedly reduced scanning time. The combination of precision, affordability, and efficiency makes the system competitive with existing approaches and, due to its accessibility and detailed methodological description, provides a distinctive contribution to the phenotyping community.
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