A nondestructive method for estimating the total green leaf area of individual rice plants using multi-angle color images

Ni Jiang; Wanneng Yang; Lingfeng Duan; Guoxing Chen; Wei Fang; Lizhong Xiong; Qian Liu

doi:doi: 10.1142/s1793545815500029

Journal of Innovative Optical Health Sciences, 2015, 8 (2): 1550002, Published Online: Jan. 10, 2019

A nondestructive method for estimating the total green leaf area of individual rice plants using multi-angle color images

论文大纲

Ni Jiang ¹Wanneng Yang ^2,3Lingfeng Duan ¹Guoxing Chen ⁴Wei Fang ¹Lizhong Xiong ²Qian Liu ^1,*

Author Affiliations

¹ Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology 1037 Luoyu Rd., Wuhan 430074, P. R. China

² National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research Huazhong Agricultural University Wuhan 430070, P. R. China

³ College of Engineering Huazhong Agricultural University Wuhan 430070, P. R. China

⁴ MOA 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. China

Agri-photonics image processing plant phenotyping regression model visible light imaging

Abstract

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 multiangle 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.

References

[1] Q. Zhang, "Strategies for developing Green Super Rice," Proc. Natl. Acad. Sci. 104(42), 16402–16409 (2007).

[2] W. Xue, Y. Xing, X. Weng, Y. Zhao, W. Tang, L. Wang, H. Zhou, S. Yu, C. Xu, X. Li, Q. Zhang, "Natural variation in Ghd7 is an important regulator of heading date and yield potential in rice," Nat. Genet. 40(6), 761–767 (2008).

[3] E. Wang, J. Wang, X. Zhu, W. Hao, L. Wang, Q. Li, L. Zhang,W. He, B. Lu, H. Lin, H. Ma, G. Zhang, Z. He, "Control of rice grain-filling and yield by a gene with a potential signature of domestication," Nat. Genet. 40(11), 1370–1374 (2008).

[4] L. Duan, W. Yang, C. Huang, Q. Liu, "A novel machine-vision-based facility for the automatic evaluation of yield-related traits in rice," Plant Methods 7, 44 (2011).

[5] F. F. Blanco, M. V. Folegatti, "Estimation of leaf area for greenhouse cucumber by linear measurements under salinity and grafting," Sci. Agric. 62(4), 305–309 (2005).

[6] S. Teng, Q. Qian, D. Zeng, Y. Kunihiro, K. Fujimoto, D. Huang, L. Zhu, "QTL analysis of leaf photosynthetic rate and related physiological traits in rice (Oryza sativa L.)," Euphytica 135(1), 1–7 (2004).

[7] T. Takai, S. Matsuura, T. Nishio, A. Ohsumi, T. Shiraiwa, T. Horie, "Rice yield potential is closely related to crop growth rate during late reproductive period," Field Crops Res. 96, 328–335 (2006).

[8] I. Jonckheere, S. Fleck, K. Nackaerts, B. Muys, P. Coppin, M. Weiss, F. Baret, "Review of methods for in situ leaf area index determination Part I. Theories, sensors and hemispherical photography," Agric. For. Meteorol. 121, 19–35 (2004).

[9] K. Beth, "Agri-photonics: Agriculture improves with photonics technologies," SPIE Professional 7, 14–17 (2009).

[10] T. Brosnan, D. W. Sun, "Inspection and grading of agricultural and food products by computer vision system — a review," Comput. Electron. Agric. 36, 193–213 (2002).

[11] S. Qi, S. Song, S. Jiang, Y. Chen, W. Li, D. Han, "Establishment of a comprehensive indicator to nondestructively analyze watermelon quality at different ripening stages," J. Innov. Opt. Health Sci. 6(4), 1350034 (2013).

[12] E. J. V. Henten, J. Bontsema, "Non-destructive crop measurements by image processing for crop growth control," J. Agric. Eng. Res. 61(2), 97–105 (1995).

[13] D. Leister, C. Varotto, P. Pesaresi, A. Niwergall, F. Salamini, "Large-scale evaluation of plant growth in Arabidopsis thaliana by non-invasive image analysis," PlantPhysiol.Biochem.37(9),671–678(1999).

[14] O. Tackenberg, "A new method for non-destructive measurement of biomass, growth rates, vertical biomass distribution and dry matter content based on digital image analysis," Ann. Bot. 99, 777–783 (2007).

[15] M. R. Golzarian, R. A. Frick, K. Rajendran, B. Berger, S. Roy, M. Tester, D. S. Lun, "Accurate inference of shoot biomass from high-throughput images of cereal plants," Plant Methods 7(2), 1–11 (2011).

[16] B. Baker, D. M. Olszyk, D. Tingey, "Digital image analysis to estimate leaf area," J. Plant Physiol. 148, 530–535 (1996).

[17] C. Leroy, L. Saint-Andre, D. Auclair, "Practical methods for non-destructive measurement of tree leaf area," Agroforestry Syst. 71, 99–108 (2007).

[18] K. Rajendran, M. Tester, S. J. Roy, "Quantifying the three main components of salinity tolerance in cereals," Plant Cell Environ. 32(3), 237–249 (2009).

[19] M. Marcon, K. Mariano, R. A. Braga, C. M. Paglis, M. S. Scalco, G. W. Horgan, "Estimation of total leaf area in perennial plants using image analysis," Revista Brasileira de Engenharia Agri{cola e Ambiental 15(1), 96–101 (2011).

[20] K. A. Nagel, A. Putz, F. Gilmer, K. Heinz, A. Fischbach, J. Pfeifer, M. Faget, S. Blossfeld, M. Ernst, C. Dimaki, B. Kastenholz, A. Kleinert, A. Galinski, H. Scharr, F. Fiorani, U. Schurr, "GROWSCREEN-Rhizo is a novel phenotyping robot enabling simultaneous measurements of root and shoot growth for plants grown in soil-filled rhizotrons," Funct. Plant Biol. 39, 891–904 (2012).

[21] G. A. Pereyra-Irujo, E. D. Gasco, L. S. Peirone, L. A. N. Aguirrezabal, "GlyPh: A low-cost platform for phenotyping plant growth and water use," Funct. Plant Biol. 39, 905–913 (2012).

[22] W. Yang, X. Xu, L. Duan, Q. Luo, S. Chen, S. Zeng, Q. Liu, "High-throughput measurement of rice tillers using a conveyor equipped with X-ray computed tomography," Rev. Sci. Instrum. 82, 025102-1– 025102-7 (2011).

[23] L. S. Caldas, C. Bravo, H. Piccilo, C. R. S. M. Faria, "Measurement of leaf area with a hand-scanner linked to a microcomputer," Revista Brasileira de Fisiologia Vegetal 4(1), 17–20 (1992).

[24] G. E. Meyer, J. C. Neto, "Verification of color vegetation indices for automated crop imaging applications," Comput. Electron. Agric. 63, 282–293 (2008).

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[J]. Journal of Innovative Optical Health Sciences, 2015, 8(2): 1550002.

A nondestructive method for estimating the total green leaf area of individual rice plants using multi-angle color images

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