Plant Phenotyping: An Active Vision Cell for Three-Dimensional Plant Shoot Reconstruction. Issue 2 (10th August 2018)

• Record Type: Journal Article
• Authors: Gibbs, Jonathon A.; Pound, Michael; French, Andrew P.; Wells, Darren M.; Murchie, Erik; Pridmore, Tony
• Is Part Of: Plant physiology. Volume 178:Issue 2(2018)
• Page Start: 524
• Page End: 534
• Publication Date: 2018-08-10
• Subjects: Plant physiology -- Periodicals; Botany -- Periodicals; Periodicals; Electronic journals; 571.2
• DOI: 10.1104/pp.18.00664
• Languages: English
• ISSNs: 0032-0889

Abstract

Abstract : 3D plant shoot reconstruction aids plant phenotyping by producing a more accurate representation of different plant types. Abstract: Three-dimensional (3D) computer-generated models of plants are urgently needed to support both phenotyping and simulation-based studies such as photosynthesis modeling. However, the construction of accurate 3D plant models is challenging, as plants are complex objects with an intricate leaf structure, often consisting of thin and highly reflective surfaces that vary in shape and size, forming dense, complex, crowded scenes. We address these issues within an image-based method by taking an active vision approach, one that investigates the scene to intelligently capture images, to image acquisition. Rather than use the same camera positions for all plants, our technique is to acquire the images needed to reconstruct the target plant, tuning camera placement to match the plant's individual structure. Our method also combines volumetric- and surface-based reconstruction methods and determines the necessary images based on the analysis of voxel clusters. We describe a fully automatic plant modeling/phenotyping cell (or module) comprising a six-axis robot and a high-precision turntable. By using a standard color camera, we overcome the difficulties associated with laser-based plant reconstruction methods. The 3D models produced are compared with those obtained from fixed cameras and evaluated by comparison with data obtained by x-ray microcomputed tomography across different plant structures. Our results show that our method is successful in improving the accuracy and quality of data obtained from a variety of plant types.