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Estimation and Validation of the Leaf Areas of Five June-bearing Strawberry (Fragaria × ananassa) Cultivars using Non-destructive Methods

일계성 딸기 5품종의 비파괴적 방법을 사용한 엽면적 추정 및 검증

  • Jo, Jung Su (Institute of Agricultural Science and Technology, Kyungpook National University) ;
  • Sim, Ha Seon (Department of Horticultural Science, College of Agricultural & Life Science, Kyungpook National University) ;
  • Jung, Soo Bin (Department of Horticultural Science, College of Agricultural & Life Science, Kyungpook National University) ;
  • Moon, Yu Hyun (Department of Horticultural Science, College of Agricultural & Life Science, Kyungpook National University) ;
  • Jo, Won Jun (Department of Horticultural Science, College of Agricultural & Life Science, Kyungpook National University) ;
  • Woo, Ui Jeong (Department of Horticultural Science, College of Agricultural & Life Science, Kyungpook National University) ;
  • Kim, Sung Kyeom (Department of Horticultural Science, College of Agricultural & Life Science, Kyungpook National University)
  • 조정수 (경북대학교 농업과학기술연구소) ;
  • 심하선 (경북대학교 원예과학과) ;
  • 정수빈 (경북대학교 원예과학과) ;
  • 문유현 (경북대학교 원예과학과) ;
  • 조원준 (경북대학교 원예과학과) ;
  • 우의정 (경북대학교 원예과학과) ;
  • 김성겸 (경북대학교 원예과학과)
  • Received : 2022.03.21
  • Accepted : 2022.04.21
  • Published : 2022.04.30

Abstract

Non-destructive estimation of leaf area is a more efficient and convenient method than leaf excision. Thus, several models predicting leaf area have been developed for various horticultural crops. However, there are limited studies on estimating the leaf area of strawberry plants. In this study, we predicted the leaf areas via nonlinear regression analysis using the leaf lengths and widths of three-compound leaves in five domestic strawberry cultivars ('Arihyang', 'Jukhyang', 'Keumsil', 'Maehyang', and 'Seollhyang'). The coefficient of determination (R2) between the actual and estimated leaf areas varied from 0.923 to 0.973. The R2 value varied for each cultivar; thus, leaf area estimation models must be developed for each cultivar. The leaf areas of the three cultivars 'Jukhyang', 'Seolhyang', and 'Maehyang' could be non-destructively predicted using the model developed in this study, as they had R2 values over 0.96. The cultivars 'Arihyang' and 'Geumsil' had slightly low R2 values, 0.938 and 0.923, respectively. The leaf area estimation model for each cultivar was coded in Python and is provided in this manuscript. The estimation models developed in this study could be used extensively in other strawberry-related studies.

엽면적의 비파괴적 추정은 잎 절단에 의한 방법에 비해 간단하고 시간을 절약하므로 다양한 원예 작물에 대한 잎 면적 예측 모델이 개발되었습니다. 그러나 딸기 잎 면적 추정에 대한 연구는 제한적이다. 본 연구는 국내산 딸기 5품종('아리향', '죽향', '금실', '매향', '설향')의 3복엽의 잎길이와 폭을 이용한 비선형회귀분석을 통해 잎면적을 예측하기 위해 수행되었다. 실제 잎 면적과 전개식을 통해 추정된 잎 면적의 관계에 대한 결정계수(R2)는 0.923에서 0.973까지 다양하였다. R2 값이 0.96 이상인 '죽향', '설향' 및 '매향' 3 품종의 잎면적은 본 연구에서 개발한 모델을 사용하여 비파괴적으로 예측할 수 있습니다. 반면 묘의 상태가 좋지 못하고 과한 생장을 했던 '아리향'과 '금실'은 각각 0.938, 0.923으로 약간 낮은 R2 값을 보였다. 본 연구에서 개발한 품종별 잎 면적 추정 모델을 Python으로 코딩하여 본 논문에 첨부하였다. 개발된 추정 모델은 많은 딸기 관련 연구에 광범위하게 사용될 수 있습니다.

Keywords

Acknowledgement

This work was supported by an Institute of Information Communications Technology Planning Evaluation (IITP) grant funded by the Korean Government (MSIT) (No. 2021-0-01578).

References

  1. Astegiano C.D., J.C. Favaro, and C.A. Bouzo 2001, Analitico: Estimacion del area foliar en distintos cultivares de tomate (Lycopersicon esculentum Mill.) utilizando medidas foliares lineales. Investigacion Agraria 16:249-256.
  2. Bhatt M., and S.V. Chanda 2003, Prediction of leaf area in phaseolus vulgaris by non-destructive method. Bulg J Plant Physiol 29:96-100.
  3. Blanco F.F., and M.V. Folegatti 2005, Estimation of leaf area for greenhouse cucumber by linear measurements under salinity and grafting. Sci Agric 62:305-309. doi:10.1590/S0103-90162005000400001
  4. Da Silva N.F., F.A. Ferreira, P.C.R. Fontes, and A.A. Cardoso 2015, Modelos para estimar a area foliar de abobora pormeio de medidas lineares. Ceres 45:259.
  5. Demirsoy H., L. Demirsoy, and A. Ozturk 2005, Improved model for the non-destructive estimation of strawberry leaf area. Fruits 60:69-73. doi:10.1051/fruits:2005014
  6. Guo D.P., and Y.Z. Sun 2001, Estimation of leaf area of stem lettuce (Lactuca sativa var angustana) from linear measurements. Indian J Agric Sci 71:483-486.
  7. Hancock J.F. 2020, Strawberries. CABI Publication, New York. pp 1-3.
  8. Keramatlou I., M. Sharifani, H. Sabouri, M. Alizadeh, and B. Kamkar 2015, A simple linear model for leaf area estimation in Persian walnut (Juglans regia L.). Sci Hortic 184:36-39. doi:10.1016/j.scienta.2014.12.017
  9. Korean Statistical Information Sercice (KOSIS) 2021, Strawberries production in Korea. Available via https://kosis.kr/index/index.do. Accessed 22 October 2021 (in Korean)
  10. NeSmith D.S. 1991, Non-destructive leaf area estimation of rabbiteye blueberries. HortScience 26:1332. https://doi.org/10.21273/HORTSCI.26.10.1332
  11. Olfati J.A., G. Peyvast, H. Shabani, and R.Z. Nosratie 2010, An estimation of individual leaf area in cabbage and broccoli using non-destructive methods. J Agric Sci Technol 12: 627-632.
  12. Olivera M., and M. Santos 1995, A semiempirical method to estimate canopy leaf area of vineyards. Am J Enol Viticult 46:389-391.
  13. Pradal C., S. Dufour-Kowalski, F. Boudon, C. Fournier, and C. Godin 2008, OpenAlea: a visual programming and component-based software platform for plant modelling. Funct Plant Biol 35:751-760. doi:10.1071/FP08084
  14. Robbins N.S., and D.M. Pharr 1987, Leaf area prediction models for cucumber from linear measurements. HortScience 22:1264-1266. https://doi.org/10.21273/HORTSCI.22.6.1264
  15. Rural Development Administration (RDA) 2022, Cultivars penetration rate of domestic strawberries. Available via https://www.rda.go.kr/board/board.do?mode=list&prgId=day_farmprmninfoEntry. Accessed 4 January 2022. (in Korean)
  16. Salerno A., C.M. Rivera, Y. Rouphael, G. Colla, M. Cardarelli, F. Pierandrei, E. Rea, and F. Saccardo 2005, Leaf area estimation of radish from simple linear measurements. Adv Hort Sci 19:213-215.
  17. Syvertsen J.P., C. Goni, and A. Otero 2003, Fruit load and canopy shading affect leaf characteristics and net gas exchange of 'Spring' navel orange trees. Tree Physiol 13:899-906. doi:10.1093/treephys/23.13.899
  18. Torri S.I., C. Descalzi, and E. Frusso 2009, Estimation of leaf area in pecan cultivars (Carya illinoinensis). Cienc Inv Agr 36:53-58. doi:10.4067/S0718-16202009000100004
  19. Tsialtas J.T., and N. Maslaris 2005, Leaf area estimation in a sugar beet cultivar by linear models. Photosynthetica 43:477-479. doi:10.1007/s11099-005-0077-z
  20. Usenik V., J. Fabcic, and F. Stampar 2008, Sugars, organic acids, phenolic composition and antioxidant activity of sweet cherry (Prunus avium L.). Food Chem 107:185-192. doi:10.1016/j.foodchem.2007.08.004
  21. Uzun S., and H. Celik 1999, Leaf area prediction models (uzcelik-1) for different horticultural plants. Turk J Agric For 23:645-650.
  22. VanRossum G., and F.L. Drake 2010, The python language reference. Python Software Foundation, Amsterdam, the Netherlands.
  23. Williams L.E. 1987, Growth of 'Thompson Seedless' grapevines. I. Leaf area development and dry weight distribution. J Amer Soc Hort Sci 112:325-330. https://doi.org/10.21273/JASHS.112.2.325
  24. World strawberry growing area and yield, Food and Agriculture Organization Corporate Statistical Database (FAOSTAT). https://www.fao.org/statistics/en/. Accessed 17 February 2022.
  25. Yun T., F. An, W. Li, Y. Sun, L. Cao, and L. Xue 2016, A novel approach for retrieving tree leaf area from ground-based LiDAR. Remote Sens 8:942. doi:10.3390/rs8110942