A nondestructive method for estimating the total green leaf area of individual rice plants using multi-angle color images
Total green leaf area (GLA) is an important trait for agronomic studies. However, existing methods for estimating the GLA of individual rice plants are destructive and labor-intensive. A nondestructive method for estimating the total GLA of individual rice plants based on multi-angle color images is...
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doaj-93b700d6f7e74ca0907fdca905effa752020-11-25T00:24:44ZengWorld Scientific PublishingJournal of Innovative Optical Health Sciences1793-54581793-72052015-03-01821550002-11550002-1210.1142/S179354581550002910.1142/S1793545815500029A nondestructive method for estimating the total green leaf area of individual rice plants using multi-angle color imagesNi Jiang0Wanneng Yang1Lingfeng Duan2Guoxing Chen3Wei Fang4Lizhong Xiong5Qian Liu6Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, 1037 Luoyu Rd., Wuhan 430074, P. R. ChinaNational Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P. R. ChinaBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, 1037 Luoyu Rd., Wuhan 430074, P. R. ChinaMOA Key Laboratory of Crop Ecophysiology and Farming, System in the Middle Reaches of the Yangtze River, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, P. R. ChinaBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, 1037 Luoyu Rd., Wuhan 430074, P. R. ChinaNational Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P. R. ChinaBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, 1037 Luoyu Rd., Wuhan 430074, P. R. ChinaTotal green leaf area (GLA) is an important trait for agronomic studies. However, existing methods for estimating the GLA of individual rice plants are destructive and labor-intensive. A nondestructive method for estimating the total GLA of individual rice plants based on multi-angle color images is presented. Using projected areas of the plant in images, linear, quadratic, exponential and power regression models for estimating total GLA were evaluated. Tests demonstrated that the side-view projected area had a stronger relationship with the actual total leaf area than the top-projected area. And power models fit better than other models. In addition, the use of multiple side-view images was an efficient method for reducing the estimation error. The inclusion of the top-view projected area as a second predictor provided only a slight improvement of the total leaf area estimation. When the projected areas from multi-angle images were used, the estimated leaf area (ELA) using the power model and the actual leaf area had a high correlation coefficient (R2 > 0.98), and the mean absolute percentage error (MAPE) was about 6%. The method was capable of estimating the total leaf area in a nondestructive, accurate and efficient manner, and it may be used for monitoring rice plant growth.http://www.worldscientific.com/doi/pdf/10.1142/S1793545815500029Agri-photonicsimage processingplant phenotypingregression modelvisible light imaging |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ni Jiang Wanneng Yang Lingfeng Duan Guoxing Chen Wei Fang Lizhong Xiong Qian Liu |
spellingShingle |
Ni Jiang Wanneng Yang Lingfeng Duan Guoxing Chen Wei Fang Lizhong Xiong Qian Liu A nondestructive method for estimating the total green leaf area of individual rice plants using multi-angle color images Journal of Innovative Optical Health Sciences Agri-photonics image processing plant phenotyping regression model visible light imaging |
author_facet |
Ni Jiang Wanneng Yang Lingfeng Duan Guoxing Chen Wei Fang Lizhong Xiong Qian Liu |
author_sort |
Ni Jiang |
title |
A nondestructive method for estimating the total green leaf area of individual rice plants using multi-angle color images |
title_short |
A nondestructive method for estimating the total green leaf area of individual rice plants using multi-angle color images |
title_full |
A nondestructive method for estimating the total green leaf area of individual rice plants using multi-angle color images |
title_fullStr |
A nondestructive method for estimating the total green leaf area of individual rice plants using multi-angle color images |
title_full_unstemmed |
A nondestructive method for estimating the total green leaf area of individual rice plants using multi-angle color images |
title_sort |
nondestructive method for estimating the total green leaf area of individual rice plants using multi-angle color images |
publisher |
World Scientific Publishing |
series |
Journal of Innovative Optical Health Sciences |
issn |
1793-5458 1793-7205 |
publishDate |
2015-03-01 |
description |
Total green leaf area (GLA) is an important trait for agronomic studies. However, existing methods for estimating the GLA of individual rice plants are destructive and labor-intensive. A nondestructive method for estimating the total GLA of individual rice plants based on multi-angle color images is presented. Using projected areas of the plant in images, linear, quadratic, exponential and power regression models for estimating total GLA were evaluated. Tests demonstrated that the side-view projected area had a stronger relationship with the actual total leaf area than the top-projected area. And power models fit better than other models. In addition, the use of multiple side-view images was an efficient method for reducing the estimation error. The inclusion of the top-view projected area as a second predictor provided only a slight improvement of the total leaf area estimation. When the projected areas from multi-angle images were used, the estimated leaf area (ELA) using the power model and the actual leaf area had a high correlation coefficient (R2 > 0.98), and the mean absolute percentage error (MAPE) was about 6%. The method was capable of estimating the total leaf area in a nondestructive, accurate and efficient manner, and it may be used for monitoring rice plant growth. |
topic |
Agri-photonics image processing plant phenotyping regression model visible light imaging |
url |
http://www.worldscientific.com/doi/pdf/10.1142/S1793545815500029 |
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