Understanding plant growth and development is crucial for insights into plant structure and function, and recent advancements in AI-driven 3D imaging technologies have revolutionized the acquisition and analysis of high-fidelity plant models. These technologies enable accurate and rapid measurement of phenotypic traits, aiding breeders in developing new varieties and helping horticulturists optimize production management. The overarching goal of this study was to establish an AI-based 3D imaging and analysis pipeline specifically designed for detailed examination of horticultural crops at the organ level within controlled environments. We developed a robotic platform equipped with a rotating base and a high-resolution camera mounted on a robotic arm, allowing comprehensive imaging from any angle around the plant. Utilizing this robot, we generated 3D models of 30 hemp plants from two growth-rate categories in controlled environments, on a weekly basis. An AI model was developed to segment these 3D models into stems, branches, and leaves. Morphological traits were extracted from each category of the segmented organs, including stem length (i.e., plant height), stem diameter, branch length, branch diameter, leaf number, leaf area, and leaf aspect ratio. These measurements contributed to a classification model capable of distinguishing between fast and regular growth rates. Experimental results showed that the 3D imaging-derived measurements were highly correlated with human-derived measurements. In addition, the extracted traits were used as quantitative descriptors to classify hemp cultivars with different growth rates in CEA. Therefore, the developed pipeline can be used as an effective and efficient tool for breeding programs and CEA production management in the future.
