Studying plant organs requires exploring the three-dimensional (3D) structures of plants. Recent advancements in computed tomography (CT) have enabled non-destructive measurements, aiding in the comprehension of plant 3D structures. This investigation focuses on the \textit{Chrysanthemum seticuspe} capitulum inflorescence, using it as a model to scrutinize the 3D arrangement of florets on receptacles within the bud structure. To delineate the 3D order of contact points, we generated slice images from CT volume data and identified receptacles and florets within them. However, manual detection of receptacles and florets is laborious due to numerous images and florets within each C. seticuspe capitulum inflorescence. Hence, we propose an automated detection method for contact points based on CT slice images utilizing image recognition techniques. Our method enhances detection accuracy by leveraging prior knowledge that contact points predominantly encircle the receptacle. Furthermore, integrating detection results facilitates estimating the 3D positions of contact points. Experimental results demonstrate improved detection accuracy by eliminating false positives. Moreover, by integrating detected contact points from slice images and employing clustering, we estimate and visualize contact point positions in 3D space.

Studying plant organs requires exploring the three-dimensional (3D) structures of plants. Recent advancements in computed tomography (CT) have enabled non-destructive measurements, aiding in the comprehension of plant 3D structures. This investigation focuses on the \textit{Chrysanthemum seticuspe} capitulum inflorescence, using it as a model to scrutinize the 3D arrangement of florets on receptacles within the bud structure. To delineate the 3D order of contact points, we generated slice images from CT volume data and identified receptacles and florets within them. However, manual detection of receptacles and florets is laborious due to numerous images and florets within each C. seticuspe capitulum inflorescence. Hence, we propose an automated detection method for contact points based on CT slice images utilizing image recognition techniques. Our method enhances detection accuracy by leveraging prior knowledge that contact points predominantly encircle the receptacle. Furthermore, integrating detection results facilitates estimating the 3D positions of contact points. Experimental results demonstrate improved detection accuracy by eliminating false positives. Moreover, by integrating detected contact points from slice images and employing clustering, we estimate and visualize contact point positions in 3D space.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.