A comparative study of plant phenotyping workflows based on three-dimensional reconstruction from multi-view images

With the world facing escalating food demand, limited agricultural land, and environmental change, there is a growing need for data-driven sustainable agricultural management. Advances in sequencing and sensor networks have reduced costs of acquiring genomic and environmental data; however, collecting phenotypic data, crucial for monitoring plant growth and detecting pests and diseases, remains labor-intensive. Technological advances have enabled efficient collection of three-dimensional (3D) data, yet this process currently involves intricate steps. Therefore, developing effective phenotyping methods is essential. In this study, we developed a phenotyping process based on 3D data, including mask image generation using deep neural network models, 3D reconstruction using the Structure from Motion/Multi-View Stereo (SfM/MVS) pipeline, and surface reconstruction for leaf area estimation. Using soybean datasets, we found that a 1/5.4x magnification effectively generated mask images. Among four mask image usage scenarios in SfM/MVS, applying soybean-and-stage masks before SfM and only soybean masks after SfM yielded the highest-quality point cloud data with the second shortest processing time. Finally, we compared Poisson reconstruction and B-spline surface fitting in leaf area estimation; B-spline fitting showed greater correlation with destructive measurements. We propose an optimal workflow for estimating leaf area and provide tools and datasets for future phenotyping research.