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Qualitative Cost-Benefit Analysis of Using Pesticidal Plants in Smallholder Crop Protection
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High-Resolution 3D Crop Reconstruction and Automatic Analysis of Phenotyping Index Using Machine Learning

by 1,2,* and 1
1
Department of Biosystems Engineering and Biomaterials Science, Seoul National University, Seoul 08826, Korea
2
Smart Agriculture Innovation Center, Kyungpook National University, Daegu 41566, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Yanbo Huang
Received: 15 September 2021 / Revised: 11 October 2021 / Accepted: 13 October 2021 / Published: 15 October 2021
(This article belongs to the Section Digital Agriculture)
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. View Full-Text
Keywords: 3D reconstruction; phenotyping; machine learning; red pepper 3D reconstruction; phenotyping; machine learning; red pepper
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MDPI and ACS Style

Yang, M.; Cho, S.-I. High-Resolution 3D Crop Reconstruction and Automatic Analysis of Phenotyping Index Using Machine Learning. Agriculture 2021, 11, 1010. https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11101010

AMA Style

Yang M, Cho S-I. High-Resolution 3D Crop Reconstruction and Automatic Analysis of Phenotyping Index Using Machine Learning. Agriculture. 2021; 11(10):1010. https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11101010

Chicago/Turabian Style

Yang, Myongkyoon, and Seong-In Cho. 2021. "High-Resolution 3D Crop Reconstruction and Automatic Analysis of Phenotyping Index Using Machine Learning" Agriculture 11, no. 10: 1010. https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11101010

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