3D plant phenotyping from a single image: learning fine-scale organ morphology with monocular depth estimation

Three-dimensional (3D) reconstruction is transforming plant science by enabling accurate phenotypic analysis and detailed morphological understanding. However, the scalability and accessibility of existing 3D reconstruction methods are limited by expensive imaging systems and constrained environments. Here we present PlantMDE, the first generalizable monocular depth estimation (MDE) model tailored for plant phenotyping. PlantMDE reconstructs 3D plant structures using only a single RGB image, eliminating the need for multi-view inputs and enabling cost-effective, scalable, and non-invasive phenotypic analysis on small-scale plants. To address a key limitation of general-purpose MDE models in capturing detailed object geometry, PlantMDE incorporates a novel organ-wise metric to explicitly estimate the 3D morphology of individual plant organs. PlantMDE is trained and evaluated on PlantDepth, a new large-scale plant RGB-D dataset comprising data from eight sources across various plant species and growing conditions. Across multiple evaluation datasets, PlantMDE significantly outperforms state-of-the-art MDE models, improving the similarity to the ground truth over Depth Anything and Marigold under both zero-shot and fine-tuning settings. Beyond reconstruction, depth features extracted by PlantMDE substantially enhance downstream phenotyping tasks, reducing error by 10.2 %–44.8 % in image-based trait estimation, including plant height, biomass, leaf area, and stress level. These results establish PlantMDE as a generalizable, scalable solution for high-throughput plant phenotyping, with broad implications for precise plant research and agriculture monitoring.