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Automatic Non-Destructive Growth Measurement of Leafy Vegetables Based on Kinect
Non-destructive plant growth measurement is essential for plant growth and health research. As a 3D sensor, Kinect v2 has huge potentials in agriculture applications, benefited from its low price and strong robustness. The paper proposes a Kinect-based automatic system for non-destructive growth mea...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5876734/ https://www.ncbi.nlm.nih.gov/pubmed/29518958 http://dx.doi.org/10.3390/s18030806 |
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author | Hu, Yang Wang, Le Xiang, Lirong Wu, Qian Jiang, Huanyu |
author_facet | Hu, Yang Wang, Le Xiang, Lirong Wu, Qian Jiang, Huanyu |
author_sort | Hu, Yang |
collection | PubMed |
description | Non-destructive plant growth measurement is essential for plant growth and health research. As a 3D sensor, Kinect v2 has huge potentials in agriculture applications, benefited from its low price and strong robustness. The paper proposes a Kinect-based automatic system for non-destructive growth measurement of leafy vegetables. The system used a turntable to acquire multi-view point clouds of the measured plant. Then a series of suitable algorithms were applied to obtain a fine 3D reconstruction for the plant, while measuring the key growth parameters including relative/absolute height, total/projected leaf area and volume. In experiment, 63 pots of lettuce in different growth stages were measured. The result shows that the Kinect-measured height and projected area have fine linear relationship with reference measurements. While the measured total area and volume both follow power law distributions with reference data. All these data have shown good fitting goodness (R(2) = 0.9457–0.9914). In the study of biomass correlations, the Kinect-measured volume was found to have a good power law relationship (R(2) = 0.9281) with fresh weight. In addition, the system practicality was validated by performance and robustness analysis. |
format | Online Article Text |
id | pubmed-5876734 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-58767342018-04-09 Automatic Non-Destructive Growth Measurement of Leafy Vegetables Based on Kinect Hu, Yang Wang, Le Xiang, Lirong Wu, Qian Jiang, Huanyu Sensors (Basel) Article Non-destructive plant growth measurement is essential for plant growth and health research. As a 3D sensor, Kinect v2 has huge potentials in agriculture applications, benefited from its low price and strong robustness. The paper proposes a Kinect-based automatic system for non-destructive growth measurement of leafy vegetables. The system used a turntable to acquire multi-view point clouds of the measured plant. Then a series of suitable algorithms were applied to obtain a fine 3D reconstruction for the plant, while measuring the key growth parameters including relative/absolute height, total/projected leaf area and volume. In experiment, 63 pots of lettuce in different growth stages were measured. The result shows that the Kinect-measured height and projected area have fine linear relationship with reference measurements. While the measured total area and volume both follow power law distributions with reference data. All these data have shown good fitting goodness (R(2) = 0.9457–0.9914). In the study of biomass correlations, the Kinect-measured volume was found to have a good power law relationship (R(2) = 0.9281) with fresh weight. In addition, the system practicality was validated by performance and robustness analysis. MDPI 2018-03-07 /pmc/articles/PMC5876734/ /pubmed/29518958 http://dx.doi.org/10.3390/s18030806 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Hu, Yang Wang, Le Xiang, Lirong Wu, Qian Jiang, Huanyu Automatic Non-Destructive Growth Measurement of Leafy Vegetables Based on Kinect |
title | Automatic Non-Destructive Growth Measurement of Leafy Vegetables Based on Kinect |
title_full | Automatic Non-Destructive Growth Measurement of Leafy Vegetables Based on Kinect |
title_fullStr | Automatic Non-Destructive Growth Measurement of Leafy Vegetables Based on Kinect |
title_full_unstemmed | Automatic Non-Destructive Growth Measurement of Leafy Vegetables Based on Kinect |
title_short | Automatic Non-Destructive Growth Measurement of Leafy Vegetables Based on Kinect |
title_sort | automatic non-destructive growth measurement of leafy vegetables based on kinect |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5876734/ https://www.ncbi.nlm.nih.gov/pubmed/29518958 http://dx.doi.org/10.3390/s18030806 |
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