High-Resolution 3D Crop Reconstruction and Automatic Analysis of Phenotyping Index Using Machine Learning

Myongkyoon Yang and Seong-In Cho
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Myongkyoon Yang: Department of Biosystems Engineering and Biomaterials Science, Seoul National University, Seoul 08826, Korea
Seong-In Cho: Department of Biosystems Engineering and Biomaterials Science, Seoul National University, Seoul 08826, Korea

Agriculture, 2021, vol. 11, issue 10, 1-22

Abstract: Beyond the use of 2D images, the analysis of 3D images is also necessary for analyzing the phenomics of crop plants. In this study, we configured a system and implemented an algorithm for the 3D image reconstruction of red pepper plant ( Capsicum annuum L.), as well as its automatic analysis. A Kinect v2 with a depth sensor and a high-resolution RGB camera were used to obtain more accurate reconstructed 3D images. The reconstructed 3D images were compared with conventional reconstructed images, and the data of the reconstructed images were analyzed with respect to their directly measured features and accuracy, such as leaf number, width, and plant height. Several algorithms for image extraction and segmentation were applied for automatic analysis. The results showed that the proposed method showed an error of about 5 mm or less when reconstructing and analyzing 3D images, and was suitable for phenotypic analysis. The images and analysis algorithms obtained by the 3D reconstruction method are expected to be applied to various image processing studies.

Keywords: 3D reconstruction; phenotyping; machine learning; red pepper (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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