Do low-cost digital tools allow measuring the cladode area?

Henrique De Souza Gomes; Renan Matheus Cordeiro Leita; Alan Cezar Bezerra; Raphaell Aymá Alves Nogueira De Carvalho; Maynar Nogueira De Carvalho; Thieres George Freire da Silva

doi:10.18011/bioeng.2024.v18.1203

Autores

Henrique De Souza Gomes 1Graduate Program in Agronomy, Federal Rural University of Pernambuco / Serra Talhada Academic Unit – UFRPE/UAST, Serra Talhada, PE, Brazil https://orcid.org/0009-0006-2647-0774
Renan Matheus Cordeiro Leita Graduate Program in Plant Production, Federal Rural University of Pernambuco / Serra Talhada Academic Unit – UFRPE/UAST, Serra Talhada, PE, Brazil https://orcid.org/0000-0001-8454-8660
Alan Cezar Bezerra Agronomy Department, Federal Rural University of Pernambuco / Serra Talhada Academic Unit – UFRPE/UAST, Serra Talhada, PE, Brazil. https://orcid.org/0000-0002-9986-9464
Raphaell Aymá Alves Nogueira De Carvalho Graduate Program in Agronomy, Federal Rural University of Pernambuco / Serra Talhada Academic Unit – UFRPE/UAST, Serra Talhada, PE, Brazil
Maynar Nogueira De Carvalho Graduate Program in Agronomy, Federal Rural University of Pernambuco / Serra Talhada Academic Unit – UFRPE/UAST, Serra Talhada, PE, Brazil
Thieres George Freire da Silva Agronomy Department, Federal Rural University of Pernambuco / Serra Talhada Academic Unit – UFRPE/UAST, Serra Talhada, PE, Brazil https://orcid.org/0000-0002-8355-4935

DOI:

https://doi.org/10.18011/bioeng.2024.v18.1203

Palavras-chave:

pear cactus, linear regression , scanner

Resumo

This study evaluated low-cost digital tools for estimating cladode area by mobile and fixed devices. We conducted the experiment at the Serra Talhada Academic Unit of the Federal Rural University of Pernambuco with pear cactus (Nopalea cochenillifera Salm-Dyck and Opuntia stricta (Haw.)). In this sense, we used four software to measure the cladode area: LAFore (fixed device), ImageJ (fixed device), Easy Leaf App (mobile device), and Petiole App (mobile device). We considered the LAFore software as a reference and used the following statistical parameters: linear regression, correlation coefficient (r), Willmott index (d), confidence index (c), and standard error of estimate (SEE). The software on fixed devices (ImageJ and LAFore) showed similar results, with performance considered excellent, and the Easy Leaf App showed a very good performance among mobile devices.

Downloads

Não há dados estatísticos.

Referências

Alvares, C. A., Stape, J. L., Sentelhas, P. C., de Moraes Gonçalves, J. L., & Sparovek, G. (2013). Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift, 22(6), 711–728. https://doi.org/10.1127/0941-2948/2013/0507 DOI: https://doi.org/10.1127/0941-2948/2013/0507

Bispo, S. V., Ferreira, M. de A., Véras, A. S. C., Batista, Â. M. V., Pessoa, R. A. S., & Bleuel, M. P. (2007). Palma forrageira em substituição ao feno de capim-elefante. Efeito sobreconsumo, digestibilidade e características de fermentação ruminal em ovinos. Revista Brasileira de Zootecnia, 36(6), 1902–1909. https://doi.org/https://doi.org/10.1590/S1516-35982007000800026 DOI: https://doi.org/10.1590/S1516-35982007000800026

Camargo, Â. P. de, & Sentelhas, P. C. (1997). Avaliação do desempenho de diferentes métodos de estimativa da evapotranspiração potencial no Estado de São Paulo. Revista Brasileira de Agrometeorologia, 5(1), 89–97.

Easlon, H. M., & Bloom, A. J. (2014). Easy Leaf Area: Automated digital image analysis for rapid and accurate measurement of leaf area. Applications in Plant Sciences, 2(7), 1400033. https://doi.org/10.3732/apps.1400033 DOI: https://doi.org/10.3732/apps.1400033

Favarin, J. L., Dourado Neto, D., García García, A., Augusto Villa Nova, N., & da Graça Guilherme Vieira Favarin, M. (2002). Equações para a estimativa do índice de área foliar do cafeeiro (1). In Pesq. agropec. bras (Issue 6). https://doi.org/10.1590/S0100-204X2002000600005 DOI: https://doi.org/10.1590/S0100-204X2002000600005

Klingler, A., Schaumberger, A., Vuolo, F., Kalmár, L. B., & Pötsch, E. M. (2020). Comparison of Direct and Indirect Determination of Leaf Area Index in Permanent Grassland. PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 88(5), 369–378. https://doi.org/10.1007/s41064-020-00119-8 DOI: https://doi.org/10.1007/s41064-020-00119-8

Leytur, M., Gandolfo, E., Carnelos, D., Giardina, E., & di Benedetto, A. (2021). Biomass accumulation of schlumbergera truncata (Haw.) moran (thanksgiving cactus) grown under high pot density. Journal of the Professional Association for Cactus Development, 23, 121–133. https://doi.org/10.56890/jpacd.v23i.331 DOI: https://doi.org/10.56890/jpacd.v23i.331

Lins, F. A. C., Silva, J. L. B. da, Moura, G. B. de A., Ortiz, P. F. S., Oliveira, J. D. A., & Alves, M. V. C. (2017). Quantile technique to precipitation, rainfall anomaly index and biophysical parameters by remote sensing in Serra Talhada, Pernambuco. Journal of Hyperspectral Remote Sensing, 7(6), 334–344. https://doi.org/10.29150/jhrs.v7.6.p334-344 DOI: https://doi.org/10.29150/jhrs.v7.6.p334-344

Lucena, L. R., Leite, M. L., Simões, V. J., & Almeida, M. C. (2018). Área de cladódio da palma Opuntia stricta utilizando dimensões lineares. Agrarian Academy, 5(09), 46-55. https://doi.org/10.18677/Agrarian_Academy_2018a5 DOI: https://doi.org/10.18677/Agrarian_Academy_2018a5

Maldaner, I. C., Heldwein, A. B., Loose, L. H., Lucas, D. D. P., Guse, F. I., & Bortoluzzi, M. P. (2009). Modelos de determinação não-destrutiva da área foliar em girassol. Ciência Rural, Santa Maria, 39(5), 1356–1361. https://doi.org/10.1590/S0103-84782009000500008 DOI: https://doi.org/10.1590/S0103-84782009000500008

Matos, R. M. de, Neto, J. D., Lima, A. S. de, Silva, P. F. da, Borges, V. E., & Sobrinho, T. G. (2017). TEOR DE UMIDADE POR DIFERENTES MÉTODOS EM NEOSSOLO DO SEMIÁRIDO BRASILEIRO. Revista Brasileira de Agricultura Irrigada, 11(4), 1588–1597. https://doi.org/10.7127/rbai.v11n400622 DOI: https://doi.org/10.7127/rbai.v11n400622

Peksen, E. (2007). Non-destructive leaf area estimation model for faba bean (Vicia faba L.). Scientia Horticulturae, 113(4), 322–328. https://doi.org/10.1016/j.scienta.2007.04.003 DOI: https://doi.org/10.1016/j.scienta.2007.04.003

Pinheiro, K. M., da Silva, T. G. F., da Silva Diniz, W. J., de Sousa Carvalho, H. F., & de Moura, M. S. B. (2015). Métodos indiretos para determinação do 00EDndice de área do cladódio da palma forrageira. Pesquisa Agropecuaria Tropical, 45(2), 163–171. https://doi.org/10.1590/1983-40632015v4530617 DOI: https://doi.org/10.1590/1983-40632015v4530617

Schneider, C. A., Rasband, W. S., & Eliceiri, K. W. (2012). NIH Image to ImageJ: 25 years of image analysis. In Nature Methods (Vol. 9, Issue 7, pp. 671–675). https://doi.org/10.1038/nmeth.2089 DOI: https://doi.org/10.1038/nmeth.2089

Silva, T. G. F. da, de Miranda, K. R., dos Santos, D. C., de Queiroz, M. G., da Silva, M. C., da Cruz Neto, J. F., & Araújo, J. E. M. (2014). Area Do Cladódio De Clones De Palma Forrageira: Modelagem, Análise E Aplicabilidade. Revista Brasileirade Ciencias Agrarias, 9(4), 633–641. https://doi.org/10.5039/agraria.v9i4a4553 DOI: https://doi.org/10.5039/agraria.v9i4a4553

Singh, J., Kumar, A., & Singh, L. (2021). Performance of the petiole mobile application on the leaf area estimation as varied with calibration height. The Pharma Innovation, 10(4S), 337–341. https://doi.org/10.22271/tpi.2021.v10.i4sf.6089 DOI: https://doi.org/10.22271/tpi.2021.v10.i4Sf.6089

Tuyogon, D. S. J. (2020). Bio-uptake of metalloids (inorganic antimony and arsenic) by plants and development of electroanalytical methods for antimony detection and speciation. The University of Liverpool (United Kingdom) ProQuest Dissertations & Theses, 2020.

Xu, X., Chen, N., Feng, J., Zhou, M., He, J., Zou, Y., Shi, S., Zhou, Y., & Jenks, M. A. (2020). Comparative analyses of leaf cuticular lipids of two succulent xerophytes of the Ordos Plateau (Gobi Desert), Tetraena mongolica maxim and Zygophyllum xanthoxylum (Bunge) Engl. Environmental and Experimental Botany, 177. https://doi.org/10.1016/j.envexpbot.2020.104129. DOI: https://doi.org/10.1016/j.envexpbot.2020.104129