A 3D printed plant model for accurate and reliable 3D plant phenotyping.

BACKGROUND: This study addresses the importance of precise referencing in 3-dimensional (3D) plant phenotyping, which is crucial for advancing plant breeding and improving crop production. Traditionally, reference data in plant phenotyping rely on invasive methods. Recent advancements in 3D sensing technologies offer the possibility to collect parameters that cannot be referenced by manual measurements. This work focuses on evaluating a 3D printed sugar beet plant model as a referencing tool. RESULTS: Fused deposition modeling has turned out to be a suitable 3D printing technique for creating reference objects in 3D plant phenotyping. Production deviations of the created reference model were in a low and acceptable range. We were able to achieve deviations ranging from -10 mm to +5 mm. In parallel, we demonstrated a high-dimensional stability of the reference model, reaching only ±4 mm deformation over the course of 1 year. Detailed print files, assembly descriptions, and benchmark parameters are provided, facilitating replication and benefiting the research community. CONCLUSION: Consumer-grade 3D printing was utilized to create a stable and reproducible 3D reference model of a sugar beet plant, addressing challenges in referencing morphological parameters in 3D plant phenotyping. The reference model is applicable in 3 demonstrated use cases: evaluating and comparing 3D sensor systems, investigating the potential accuracy of parameter extraction algorithms, and continuously monitoring these algorithms in practical experiments in greenhouse and field experiments. Using this approach, it is possible to monitor the extraction of a nonverifiable parameter and create reference data. The process serves as a model for developing reference models for other agricultural crops.

Objective computerised assessment of residual ridge resorption in the human maxilla and maxillary sinus pneumatisation.

OBJECTIVES: Atrophic resorption of the maxillary alveolar ridge is a complication that makes implantological rehabilitation critical. Our aim was to develop a novel computer aided procedure for the accurate quantitative assessment of maxillary residual ridge resorption including pneumatisation of the maxillary sinus that goes beyond previously described approaches and to apply it to a large dataset. MATERIALS AND METHODS: To develop and refine the method, we performed a retrospective analysis using computed tomography data from 405 patients to generate segmented, three-dimensional models of zygomaticomaxillary bones and maxillary sinuses. Using anatomical landmarks and orientation lines or planes, all models were aligned automatically to subsequently generate cross-sectional images (n = 2835), enabling the classification of atrophy as well as the quantification of volumes and caudal extensions of the maxillary sinuses. RESULTS: We developed and implemented an accurate and reproducible workflow for the semi-automated analysis of volumetric maxillary images. Comprehensive statistical analysis of the large quantitative dataset revealed various correlations of maxillary process heights and sinus volumes with atrophy class, age and region and identified conjectural trends over the patient group. CONCLUSIONS: The method was used successfully to process a large dataset to classify atrophy, to measure alveolar height parameters, and to quantify maxillary sinus volume, bottom volume and pneumatisation. CLINICAL RELEVANCE: Apart from the anthropometric value of the generated dataset, the method could be applied to provide additional and more accurate data to assess the necessity of bone augmentation in the context of three-dimensional planning before implantation.