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- 2021-11-22 (2)
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ImageJ software as an alternative method for estimating leaf area in oats
Software ImageJ como método alternativo para estimar área foliar en avena
DOI:
https://doi.org/10.15446/acag.v69n3.69401Palabras clave:
Avena strigosa Schreb, Avena sativa L., leaf area meter, LI-COR 3100 (en)Avena strigosa Schreb, Avena sativa L., leaf area meter, LI-COR 3100 (es)
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The aim of this study was to compare two methods of estimating leaf area (leaf area meter LI-COR 3100 and ImageJ software) in oats. Renascem black oats and UTF Iguaçu white oats cultivars were evaluated. All the leaves of one plant per plot, with six repetitions, were evaluated every seven days to obtain five assessments for each cultivar. The leaves were identified and fixed on a white A4 sheet of paper with an indication of a known area (45 cm²), photographed and evaluated using a leaf area meter (LI-3100 - LI-COR). Leaf area was then estimated using ImageJ software. Estimates were obtained for standard deviation and coefficient of variation, and a simple linear regression equation was estimated based on the two estimation methods. Mean variances were tested using the ‘F’-test and the means compared by the ‘t’-test. There was no difference between the leaf areas found by both methods. In addition, they were highly correlated, and variances were homogeneous. We concluded that ImageJ software can be used instead of the leaf area meter on the two oat cultivars evaluated.
El objetivo del estudio fue comparar dos métodos (Integrador del área foliar LI-COR 3100 y el software ImageJ) para estimar el área foliar en el cultivo de avena (Avena sativa L.). Para el efecto se evaluaron dos variedades de avena, una negra (Renascem) y otra blanca (UTF Iguaçu). Se utilizaron seis repeticiones (diseño completamente aleatorio), con la recolección de todas las hojas de una planta seleccionada al azar por parcela cada 7 días y un total de cinco evaluaciones para cada variedad. Las hojas cosechadas fueron identificadas y fijadas sobre una hoja A4 blanca, con una marca de área conocida (45 cm²), fotografiadas y evaluadas utilizando el integrador de área de la hoja (medidor de área LI3100 - LI-COR). Posteriormente en las mismas hojas se midieron las áreas foliares utilizando el software ImageJ. Con los datos obtenidos en cada método se calcularon la desviación estándar, el coeficiente de variación, y la ecuación de regresión lineal simple. Las varianzas de los valores se probaron con la prueba ‘F’ y las medias se compararon con la prueba ‘t’. Al considerar todas las hojas evaluadas por ambos métodos, se encontró que las áreas de las hojas no difieren entre ellas cuando se comparan con los métodos propuestos. Además, tienen altas correlaciones y las variaciones son homogéneas. Como conclusiones, se encontró que el método que usa el software ImageJ puede ser utilizado para reemplazar el integrador del área de la hoja para las variedades de avena evaluados.
Referencias
Adami M.; Hastenreiter, F. A.; Danilton, L. F.; Faria, R. T. 2007. Estimativa de área foliar de soja usando imagens digitais e dimensões foliares. Anais XII Simpósio brasileiro de sensoriamento remoto. Florianópolis, Brasil, INPE, 9-14. http://marte.sid.inpe.br/col/dpi.inpe.br/sbsr@80/2006/11.16.01.08.52/doc/9-14.pdf
Benincasa, M. M. P. 1988. Análise do crescimento de plantas: noções básicas. FUNEP. Jaboticabal, Brasil, 42p.
Bignami, C.; Rossini, F. 1996. Image analysis of leaf area index and plant size of young hazelnut plants. Journal Horticulture Science and Biotechnology, 71(1), 113-121. http://dx.doi.org/10.1080/14620316.1996.11515387
Blanco, F. F.; Folegatti, M.V. 2003. A new method for estimating the leaf area index of cucumber and tomato plants. Horticultura Brasileira, 21(4), 666-669. http://dx.doi.org/10.1590/S0102-05362003000400019
Coelho-Filho, M. A.; Angelocci, L. R.; Vasconcelos, M. R. B.; Coelho, E. F. 2005. Leaf area estimative of young 'Tahiti' lime using non-destructive methods. Revista Brasileira de Fruticultura, 27(1), 163-167. http://dx.doi.org/10.1590/S0100-29452005000100043
Favarin, J. L.; Dourado Neto, D.; García, A. G.; Villa Nova, N. A.; Favarin, M. G. G. V. 2002. Equations for estimating the coffee leaf area index. Pesquisa Agropecuária Brasileira, 37(6), 769-773. http://dx.doi.org/10.1590/S0100-204X2002000600005
Gomes Filho, A.; Yanagiwara, R. S.; Bôas, R. L. V.; Backes, C.; Lima, C. P. 2006. Validation of the method of numerical scale for incidence quantification of skin freckles on papaya through the use of digital images. Revista Brasileira de Fruticultura, 28(3), 365-368. http://dx.doi.org/10.1590/S0100-29452006000300007
Jesus-Junior, W. C.; Vale, F. X. R.; Coelho, R. R.; Costa, L. C. 2001. Comparison of two methods for estimating leaf area index on common bean. Agronomy journal, 93(5), 989-991. http://dx.doi.org/10.2134/agronj2001.935989x
Kvet, J.; Marshall, J. K. 1971. Assessment of leaf area and other assimilating plant surfaces. In: Sestak, Z., Catsky J, Jarvis PG (Eds.) Plant photosynthetic production: Manual of methods. Junk, The Hague, p.517-555.
LI-COR. LI 3100. 1996. Area meter instruction manual. Lincoln: LI-COR, 34p.
Lino, A. C. L.; Sanches, J.; Fabbro, I. M. D. 2008. Image processing techniques for lemons and tomatoes classification. Bragantia, 67(3), 785-789. http://dx.doi.org/10.1590/S0006-87052008000300029
Lopes, S. J.; Brum, B.; Santos, V. J.; Fagan, E. B.; Luz, G. L.; Medeiros, S. L. P. 2007. Estimate of the leaf area of melon plant in growing stages for digital photos. Ciência Rural, 37(4), 1153-1156. http://dx.doi.org/10.1590/S0103-84782007000400039
Mielke, M. S.; Hoffman, A.; Endres, J. C.; Fachinello, J. C. 1995. Comparison of laboratory and field methods for the estimation of leaf area of wild fruit species. Scientia Agricola, 52(1), 82-88. http://dx.doi.org/10.1590/S0103-90161995000100015
Monteiro, J. E. B.; Sentelhas, P. C.; Chiavegato, E. J.; Guiselini, C.; Santiago, A. V.; Prela, A. 2005. Cotton leaf area estimates based on leaf dimensions and dry mass methods. Bragantia, 64(1), 15-24. http://dx.doi.org/10.1590/S0006-87052005000100002
Moreira-Filho, E. C.; Silva, D. S.; Pereira, W. E.; Cabral Júnior, C. R.; Andrade, M. V. M.; Silva, G. E.; Viana, B. L. 2007. Leaf area estimation in flor de seda (Calotropis procera). Archivos de Zootecnia, 56(214), 245-248. https://www.redalyc.org/pdf/495/49521414.pdf
Norman, J. M.: Campbell, G. S. 1989. Canopy structure. In: Pearcy, R. W.; Ehleringer, J. R.; Mooney, H. A.; Rundel, P. W. (Eds.) Plant Physiological Ecology. Chapman and Hall. P. 301-325. https://doi.org/10.1007/978-94-009-2221-1
Partelli, F. L.; Vieira, H. D.; Detmann, E.; Campostrini, E. 2006. Estimativa da área foliar do cafeeiro conilon a partir do comprimento da folha. Revista Ceres, 53(306), 204-210. http://www.ceres.ufv.br/ojs/index.php/ceres/article/view/3131/1026
Ramirez, G. M.; Zullo Júnior, J. 2010. Estimation of biophysical parameters of coffee fields based on high-resolution satellite images. Engenharia Agrícola, 30(3), 468-479. http://dx.doi.org/10.1590/S0100-69162010000300011
Sbrissia, A. F.; Silva, S. C. 2008. Comparison of three methods for estimating leaf area index of marandu palisadegrass swards under continuous stocking. Revista Brasileira de Zootecnia, 37(2), 212-220. http://dx.doi.org/10.1590/S1516-35982008000200006
Tavares-Junior, J. E.; Favarin, J. L.; Dourado Neto, D.; Maia, A. H. N.; Fazouli, L. C.; Bernardes, M. S. 2002. Comparative analysis among methods of estimating coffee-tree leaf area. Bragantia, 61(2), 199-203. http://dx.doi.org/10.1590/S0006-87052002000200013
Tewolde, H.; Sistani, K. R.; Rowe, D. E.; Adeli, A.; Tesgaye, T. 2005. Estimating cotton leaf area index nondestructively with a light sensor. Agronomy Journal, 97(1), 1158–1163. http://dx.doi.org/10.2134/agronj2004.0112
Wilhelm, W. W.; Ruwe, K.; Schlemmer, M. R. 2000. Comparison of three leaf area index meters in a corn canopy. Crop Science, 40(1), 1179–1183. http://dx.doi.org/10.2135/cropsci2000.4041179x
Yamane, T. (1969). Statistics an introductory analysis. 2ª Ed. New York, Harper, 1130p. https://www.gbv.de/dms/zbw/252560191.pdf
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