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Leafy vegetables are an essential source of the various nutrients that people need in their daily lives. The quantification of vegetable phenotypes and yield estimation are prerequisites for the selection of genetic varieties and for the improvement of planting methods. The traditional method is manual measurement, which is time-consuming and cumbersome. Therefore, there is a need for efficient and convenient in situ vegetable phenotype identification methods to provide data support for breeding research and for crop yield monitoring, thereby increasing vegetable yield. In this paper, a novel approach was developed for the in-situ determination of the three-dimensional (3D) phenotype of vegetables by recording video clips using smartphones. First, a smartphone was used to record the vegetable from different angles, and then the key frame containing the crop area in the video was obtained using an algorithm based on the vegetation index and scale-invariant feature transform algorithm (SIFT) matching. After obtaining the key frame, a dense point cloud of the vegetables was reconstructed using the Structure from Motion (SfM) method, and then the segmented point cloud and a point cloud skeleton were obtained using the clustering algorithm. Finally, the plant height, leaf number, leaf length, leaf angle, and other phenotypic parameters were obtained through the point cloud and point cloud skeleton. Comparing the obtained phenotypic parameters to the manual measurement results, the root-mean-square error (RMSE) of the plant height, leaf number, leaf length, and leaf angle were 1.82, 1.57, 2.43, and 4.7, respectively. The measurement accuracy of each indicators is greater than 80%. The results show that the proposed method provides a convenient, fast, and low-cost 3D phenotype measurement pipeline. Compared to other methods based on photogrammetry, this method does not need a labor-intensive image-capturing process and can reconstruct a high-quality point cloud model by directly recording videos of crops.

The detection of architecture was one of the essential questions of plant root phenotyping research. The classical root architecture detection method was carried out by manual measurement. It is not only tedious, but also has a poor reliability, and the roots are damaged easily. This paper described a three-dimensional architecture measurement method based on XCT and centerline extraction method. The method includes the following steps: (1) obtaining the root CT images through the XCT system; (2) obtaining a root three-dimensional model after image segmentation and reconstruction. To solve the problem of model fracture, the quality of the reconstruction model was improved by a series of pre-processing methods; (3) extracting the root’s centerline based on the mesh contraction, and the high-quality centerline was obtained after the post-processing methods; (4) calculating the architecture parameters. Different root samples were tested to validate the method for centerline extraction, and the root architecture was calculated by the centerline. The results were compared with the manual measurements, and the mean absolute percentage error of root length and root angle were 1.74% and 4.51, respectively. The entire algorithm runs for less than 30 seconds. The study may provide an effective method for root architecture detection. Keywords: CT Images; three-dimensional root model; centerline extraction; root architecture; plant root phenotyping DOI:  10.33440/j.ijpaa.20190202.38.  Citation: Yang Z S, Zhou X C, Chen F Q, Han Y X. Centerline extraction based three-dimensional architecture parameter measurement method for plant roots.  Int J Precis Agric Aviat, 2019; 2(2): 11–18.