A model-based approach to recovering the structure of a plant from images
This addresses the challenge of phenotype analysis in plants, particularly for species like wheat with thin elements, but is incremental as it builds on existing model-based and silhouette-based techniques.
The authors tackled the problem of recovering plant structure from a small set of widely-spaced images, focusing on wheat, and developed a method using silhouettes and a generate-and-test approach that efficiently produces accurate estimates without manual intervention.
We present a method for recovering the structure of a plant directly from a small set of widely-spaced images. Structure recovery is more complex than shape estimation, but the resulting structure estimate is more closely related to phenotype than is a 3D geometric model. The method we propose is applicable to a wide variety of plants, but is demonstrated on wheat. Wheat is made up of thin elements with few identifiable features, making it difficult to analyse using standard feature matching techniques. Our method instead analyses the structure of plants using only their silhouettes. We employ a generate-and-test method, using a database of manually modelled leaves and a model for their composition to synthesise plausible plant structures which are evaluated against the images. The method is capable of efficiently recovering accurate estimates of plant structure in a wide variety of imaging scenarios, with no manual intervention.