Non-destructive Leaf Area Measurement Using Mathematical Modeling for Paddy Varieties

Year 2023, , 339 - 342, 01.07.2023

Fatih Öner , Mehmet Serhat Odabaş

https://doi.org/10.47115/bsagriculture.1280946

Cited By: 1

Abstract

Leaf area is considered an important parameter in fields such as plant phenotyping and plant breeding. In this study, leaf areas of different rice varieties were measured using a leaf area meter. Subsequently, a mathematical model was developed using leaf dimensions to estimate leaf area. Multiple regression analysis was used in the study to examine how leaf area is related to leaf dimensions. The results showed significant differences in leaf areas among different paddy varieties (Efe, Osmancık-97, Hamzadere, and Paşalı). Additionally, leaf dimensions were found to be a strong predictor for estimating leaf area. The equation of leaf area (LA= a + (b x L) + (c x W) + (d x L²) + [e x (L x W)] for paddy varieties tested. The R² values for paddy varieties between 84% - 99%. The mathematical model is an important tool that can be used in plant phenotyping and plant breeding, and can be further utilized in future research in these fields.

Keywords

Paddy varieties, Leaf area, Modeling

References

• Albayrak S, Yüksel O. 2009. Leaf area prediction model for sugar beet and fodder beet. SDU J Graduated Instit Sci, 13 (1): 20-24.
• Bozkurt S, Sayılıkan Mansuroğlu G. 2019. Creating of leaf area model with linear measurements in pepper plant. MKU J Agri Sci, 24(2): 77-86.
• Broge NH, Leblanc E. 2001. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens Environ, 76: 156-172.
• Causton, DR, Venus JC. 1981. The biometry of plant growth. Edward Arnold Publications, London, UK, pp: 272.
• FAO. 2020. Food and Agriculture Organization of the United Nations (FAO Statistical Database, Available from http://faostat.fao.org) (accessed date: April 10, 2023).
• Fuzino K, Yamanouchi U, Yano M. 2012. Roles of the Hd5 gene controlling heading date for adaptation to the northern limits of rice cultivation. Theoret Applied Genet, 126: 611-618.
• Gutierrez-Boem F. H. Thomas G.W. 2001. Leaf Area Development in soybean as affected by phosphorus nutrition and water deficit. J Plant Nutri, 24(11): 1711–1729.
• Kandiannan K, Kailasam C, Chandaragiri KK, Sankaran H. 2002. Allometric model for leaf area estimation in black papper (Piper nigrum L.). J Agron Crop Sci, 188:138-142.
• Lopez LAM, Rivera RM, Herrera OR, Naval WT (2019) Relationship between growth traits and yield formation in Indica-type rice crop. Agron Mesoam, 230(1): 79–100.
• Myneni RB, Nemani RR, Running SW (1997). Estimation of global leaf area index and absorbed PAR using radiative transfer models. IEEE Transact Geosci Remote Sens, 35:1380-1393.
• Odabas MS, Kayhan G, Ergun E, Senyer N. 2016. Using artificial neural network and multiple linear regression for predicting the chlorophyll concentration index of saint john’s wort leaves. Commun Soil Sci Plant Anal, 47(2): 237-245.
• Odabas, M.S. Kevsereoğlu, K. Çırak, C. Sağlam B. 2005. Non-destructive estimation of leaf area in some medicinal plants. Turkish J Field Crops, 10, 29-36.
• Öner F. Gülümser A. Sezer İ. Odabas M.S. Akay H. Açıkgöz M.A. 2012. Estimation of corn (Zea mays L.) leaf area with mathematical modeling. Res J Agri Sci, 5 (1): 128-130.
• Rahman M.A. Salim Khan U.M. Hossain M. S. Alam Rahman M. H. Mridha A.H. 2012. Estimation of leaf area index and chlorophyll content of wheat at different growth stages using non-destructive methods. Plant Prod Sci, 15: 48-56.
• Sezer İ. Akay H. Öner F. Şahin M. 2012. Rice production systems. Turkish J Sci Rev, (2): 6-11.
• TMO. 2023. https://www.tmo.gov.tr/Upload/Document/istatistikler/tablolar/6celtikeuva.pdf (accessed date: April 10, 2023).
• Uzun S. Çelik H. 1999. Leaf area prediction models (Uzçelik-I) for different horticultural plants. Tr J Agri Fores, 23: 645-650.
• Uphoff N. 2003. Higher yields with fewer external inputs? The system of rice intensification and potential contributions to agricultural sustainability. Inter J Agri Sustain, 1(1): 38-50.