Kochi, N. | Search bioRxiv

bioRxiv2025

Multi-omics profiling with indoor-unmanned phenotyping reveals drought adaptation through constitutive ABF1 expression in wild rice

Improving drought resistance is crucial for stable crop production under climate change. Identifying the mechanisms of drought resistance using diverse genetic resources, including crop wild relatives, would be beneficial for molecular breeding. Here, we developed an indoor, unmanned phenotyping platform that can noninvasively and automatically collect temporal data on plant responses to drought stress. Using this system, we analyzed the phenotypic, transcriptomic, and environmental data of four cultivated rice varieties and five wild relatives. Multi-omics analysis revealed that one wild rice species exhibited drought adaptation through the constitutive expression of ABSCISIC ACID RESPONSIVE ELEMENT-BINDING FACTOR 1 (ABF1), which encodes a transcription factor that regulates drought resistance, before drought stress. Drought testing of introgression lines of cultivated rice with constitutive ABF1 expression revealed higher drought tolerance than in cultivars without a growth penalty. Our findings suggest that constitutive ABF1 expression contributes to drought adaptation in both cultivated and wild rice.

plant biology↗

bioRxiv2024

MMM and CLCFM: A 3D point cloud reconstruction method based on photogrammetry for all-around images taken by rotating an individual plant

This research aims to develop a novel technique to acquire a large amount of high-density, high-precision 3D point cloud data for plant phenotyping using photogrammetry technology. The complexity of plant structures, characterized by overlapping thin parts such as leaves and stems, makes it difficult to reconstruct accurate 3D point clouds. One challenge in this regard is occlusion, where points in the 3D point cloud cannot be obtained due to overlapping parts, preventing accurate point capture. Another is the generation of erroneous points in non-existent locations due to image-matching errors along object outlines. To overcome these challenges, we propose a 3D point cloud reconstruction method, CLCFM3 (Closed-Loop Coarse-to-Fine method with Multi-Masked Matching). This method repeatedly executes a process that generates point clouds locally to suppress occlusion (multi-matching) and a process that removes noise points using a mask image (masked matching). Furthermore, we propose the Closed-Loop Coarse-to-Fine Method (CLCFM) to improve the accuracy of structure from motion, which is essential for implementing the proposed point cloud reconstruction method. CLCFM solves loop-closure by performing coarse-to-fine camera position estimation. By facilitating the acquisition of high-density, high-precision 3D data on a large number of plant bodies, as is necessary for research activities, this approach is expected to enable comparative analysis of visible phenotypes in the growth process of a wide range of plant species based on 3D information.

plant biology↗