Correlación del contenido de clorofila foliar de la especie Coffea arabica con índices espectrales en imágenes

  • Andrés Felipe Solis Corporación Universitaria Comfacauca
  • David Armando Revelo luna Corporación Universitaria Comfacauca
  • Diego Andrés Campo Ceballos Corporación Universitaria Comfacauca
  • Carlos Alberto Gaviria López Universidad del Cauca
Palabras clave: Contenido de clorofila, Índices de Vegetación, Espectrofotometría Visible, Imágenes multiespectrales, Correlación, Cultivos de campo, Agricultura de precisión, Cámaras multiespectrales, NDVI, GARI, GRNDVI

Resumen

La clorofila es un pigmento fundamental para los procesos fotosintéticos de las especies vegetales, y constituye una limitante en la producción agrícola. La estimación del contenido de clorofila foliar (LCC) se realiza generalmente mediante técnicas invasivas de espectrofotometría. Las imágenes multiespectrales y los Índices de Vegetación (IV), constituyen una alternativa importante debido a que permiten la estimación in situ del pigmento. En este trabajo se pretende encontrar la variabilidad y relaciones entre el contenido localizado de clorofila, de la especie Coffea arabica, e IV tomados de imágenes multiespectrales. Se realizó un muestreo de hojas al azar, y se seleccionaron hojas sanas y enfermas. Se estimó el LCC de 60 muestras mediante espectrofotometría y se encontró el coeficiente de correlación con IV. Los mejores indicadores del pigmento fueron los índices GARI, GNDVI y NDVI, entre 14 índices estudiados. Se encontró que la variabilidad de los datos de IV en diferentes zonas de hojas enfermas, concuerda con la distribución de clorofila no homogénea en esas hojas, ya que la degradación de clorofila en esta variedad no se comporta de forma isotrópica. Este resultado alienta la posibilidad de usar esta técnica para inferir el estado de salud de esta planta.

Descargas

Los datos de descargas todavía no están disponibles.

Referencias bibliográficas

AL-SHIDI, RASHID-HAMDAN; KUMAR, LALIT; AL-KHATRI, SALIM. Detecting Dubas bug infestations using high resolution multispectral satellite data in Oman. Computers and Electronics in Agriculture, v. 157, 2019, p. 1-11.https://doi.org/10.1016/j.compag.2018.12.037

ARNON, DANIEL. Copper enzymes in isolated chloroplasts. Polyphenoloxidase in beta vulgaris. Plant Physiology, v. 24, n. 1, 1949, p. 1-15.https://doi.org/10.1104/pp.24.1.1

ASHAPURE, AKASH; JUNG, JINHA; CHANG, ANJIN; OH, SUNGCHAN; MAEDA, MURILO; LANDIVAR, JUAN A. Comparative Study of RGB and Multispectral Sensor-Based Cotton Canopy Cover Modelling Using Multi-Temporal UAS Data. Remote Sensing, v. 11, n. 23, 2019, p. 2757.https://doi.org/10.3390/rs11232757

BHATNAGAR, SAHEBA; GHOSH, BIDISHA; REGAN, SHANE; NAUGHTON, OWEN; JOHNSTON, PAUL; GILL, LAURENCE. Monitoring environmental supporting conditions of a raised bog using remote sensing techniques. Proceedings of the International Association of Hydrological Sciences, v. 380, 2018, p. 9–15.https://doi.org/10.5194/piahs-380-9-2018

CARUSO, GIOVANNI; TOZZINI, LETIZIA; RALLO, GIOVANNI; PRIMICERIO, JACOPO; MORIONDO, MARCO; PALAI, G.; GUCCI, RICCARDO. Estimating biophysical and geometrical parameters of grapevine canopies (‘Sangiovese’) by an unmanned aerial vehicle (UAV) and VIS-NIR cameras. Vitis, v. 56, n. 2, 2017, p. 63–70.https://doi.org/10.5073/vitis.2017.56.63-70

CEBALLOS-CAMPO, DIEGO-ANDRÉS; GAVIRIA-LÓPEZ, CARLOS-ALBERTO. Optimización de las condiciones de tiempo y temperatura en el proceso de tostado de café del cauca, teniendo en cuenta la percepción del consumidor. Cartagena (Colombia): 2° Congreso Latinoamericano de Ingeniería, 2019.

CHEMURA, ABEL; MUTANGA, ONISIMO; DUBE, TIMOTHY. Remote sensing leaf water stress in coffee (Coffea arabica) using secondary effects of water absorption and random forests. Physics and Chemistry of the Earth, Parts A/B/C, v. 100, 2017, p. 317-324.https://doi.org/10.1016/j.pce.2017.02.011

CHEMURA, ABEL; MUTANGA, ONISIMO; ODINDI, JOHN. Modelling Leaf Chlorophyll Content in Coffee (Coffea Arabica) Plantations Using Sentinel 2 Msi Data. Memorias IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. Valencia (España): 2018, p. 8228-8231.https://doi.org/10.1109/IGARSS.2018.8518848

CHEMURA, ABEL; MUTANGA, ONISIMO; ODINDI, JOHN; KUTYWAYO, DUMISANI. Mapping spatial variability of foliar nitrogen in coffee (Coffea arabica L.) plantations with multispectral Sentinel-2 MSI data. ISPRS Journal of Photogrammetry and Remote Sensing, v. 138, 2018, p. 1-11.https://doi.org/10.1016/j.isprsjprs.2018.02.004

CHOI, MYUNGJIN; YOUNG-KIM, RAE; NAM, MYEONG-RYONG; HONG-OH, KIM. Fusion of Multispectral and Panchromatic Satellite Images Using the Curvelet Transform. IEEE Geoscience and Remote Sensing Letters, v. 2, n. 2, 2005, p. 136-140.https://doi.org/10.1109/LGRS.2005.845313

CHOUBIN, BAHRAM; SOLEIMANI, FREIDOON; PIRNIA, ABDOLLAH; SAJEDI-HOSSEINI, FARZANEH; ALILOU, HOSSEIN; RAHMATI, OMID; MELESSE, ASSEFA; P. SINGH, VIJAY; SHAHABI, HIMAN. Effects of drought on vegetative cover changes: Investigating spatiotemporal patterns. Extreme Hydrology and Climate Variability, Elsevier, 2019, p. 213-222.

CROSS, MATTHEW; SCAMBOS, THEODORE; PACIFICI, FABIO; MARSHALL, WESLEY. Determining Effective Meter-Scale Image Data and Spectral Vegetation Indices for Tropical Forest Tree Species Differentiation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, v. 12, n. 8, 2019, p. 2934-2943.https://doi.org/10.1109/JSTARS.2019.2918487

DE LA CASA, A.; OVANDO, G.; BRESSANINI, L.; MARTÍNEZ, J.; DÍAZ, G.; MIRANDA, C. Soybean crop coverage estimation from NDVI images with different spatial resolution to evaluate yield variability in a plot. ISPRS Journal of Photogrammetry and Remote Sensing, v. 146, 2018, p. 531-547.https://doi.org/10.1016/j.isprsjprs.2018.10.018

DE SOUZA, ROMINA; PEÑA-FLEITAS, TERESA; THOMPSON, RODNEY; GALLARDO, MARISA; GRASSO, RAFAEL; PADILLA , FRANCISCO. The Use of Chlorophyll Meters to Assess Crop N Status and Derivation of Sufficiency Values for Sweet Pepper. Sensors, v. 19, n. 13, 2019, p. 2949.https://doi.org/10.3390/s19132949

FONG, YOUYI; HUANG, YING. Modified Wilcoxon–Mann–Whitney Test and Power Against Strong Null. The American Statistician, v. 73, n. 1, 2019, p. 43-49.https://doi.org/10.1080/00031305.2017.1328375

GARCÍA-CÁRDENAS, DIEGO-ALEJANDRO; RAMÓN-VALENCIA, JACIPT-ALEXANDER; ALZATE-VELÁSQUEZ, DIEGO-FERNANDO; PALACIOS-GONZALEZ, JORDI-RAFAEL. En: Dynamics of the Indices NDVI and GNDVI in a Rice Growing in Its Reproduction Phase from Multi-spectral Aerial Images Taken by Drones. Advances in Information and Communication Technologies for Adapting Agriculture to Climate Change II. Cham (Switzerland): Springer, 2018, p. 106-119.https://doi.org/10.1007/978-3-030-04447-3_7

GITELSON, ANATOLY; KAUFMAN, YORAM; MERZLYAK, MARK. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, v. 58, n. 3, 1996, p. 289-298.https://doi.org/10.1016/S0034-4257(96)00072-7

GOEL, NARENDRA; QIN, WENHAN. Influences of canopy architecture on relationships between various vegetation indices and LAI and FPAR: A computer simulation. Remote Sensing Reviews, v. 10, n. 4, 1994, p. 309–347.https://doi.org/10.1080/02757259409532252

GONZÁLEZ-RIVERA, MIGUEL; DELGADO-RAMÍREZ, GERARDO; MIGUEL-VALLE, ENRIQUE; SERVIN-PRIETO, ALAN-JOEL; REYES-GONZÁLEZ, ARTURO; SERVÍN-PALESTINA, MIGUEL; ESTRADA-ÁVALOS, JUAN. Estimación de la variación espacial y temporal de la concentración de nitrógeno en maíz forrajero mediante sensoria remota. Agrofaz: publicación semestral de investigación científica, v. 17, n. 2, 2017, p. 53–59.

GONZÁLEZ-ESTRADA, ELIZABETH; COSMES, WALDENIA. Shapiro–Wilk test for skew normal distributions based on data transformations. Journal of Statistical Computation and Simulation, v. 89, n. 17, 2019, p. 3258-3272.https://doi.org/10.1080/00949655.2019.1658763

GROSSMANN, KATJA; FRANKENBERG, CHRISTIAN; MAGNEY, TROY; HURLOCK, STEPHEN; SEIBT, ULRIKE; STUTZ, JOCHEN. PhotoSpec: A new instrument to measure spatially distributed red and far-red Solar-Induced Chlorophyll Fluorescence. Remote Sensing of Environment, v. 216, 2018, p. 311-327.https://doi.org/10.1016/j.rse.2018.07.002

HUNT, RAYMOND; DAUGHTRY, CRAIG; EITEL, JAN; LONG, DAN. Remote Sensing Leaf Chlorophyll Content Using a Visible Band Index. Agronomy Journal, v. 103, n. 4, 2011, p. 1090-1099.https://dx.doi.org/10.2134/agronj2010.0395

IHUOMA, SAMUEL; MADRAMOOTOO, CHANDRA. Narrow-band reflectance indices for mapping the combined effects of water and nitrogen stress in field grown tomato crops. Biosystems Engineering, v. 192, 2020, p. 133-143.https://doi.org/10.1016/j.biosystemseng.2020.01.017

JEFFREY, S.W.; HUMPHREY, G.F. New spectrophotometric equations for determining chlorophylls a, b, c1 and c2 in higher plants, algae and natural phytoplankton. Biochemie und Physiologie der Pflanzen, v. 167, n. 2, 1975, p. 191-194.https://doi.org/10.1016/S0015-3796(17)30778-3

JIMÉNEZ-SUANCHA, SONIA-CONSTANZA; HUMBERTO-ALVARADO, OSCAR; BALAGUERA-LÓPEZ, HELBER-ENRIQUE. Fluorescence as an indicator of stress in Helianthus annuus L. A review. Revista Colombiana de Ciencias Hortícolas, v. 9, n. 1, 2015, p. 149–160.https://doi.org/10.17584/rcch.2015v9i1.3753

KAUFMAN, Y.J.; TANRE, D. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing, v. 30, n. 2, 1992, p. 261-270.https://doi.org/10.1109/36.134076

KUAI, BENKE; CHEN, JUNYI; HÖRTENSTEINER, STEFAN. The biochemistry and molecular biology of chlorophyll breakdown. Journal of Experimental Botany, v. 69, n. 4, 2018, p. 751-767.https://doi.org/10.1093/jxb/erx322

MARCHEAFAVE, GUSTAVO; TORMENA, CLÁUDIA; PAULI, ELIS-DAIANE; RAKOCEVIC, MIROSLAVA; BRUNS, ROY; SCARMINIO, IEDA. Experimental mixture design solvent effects on pigment extraction and antioxidant activity from Coffea arabica L. leaves. Microchemical Journal, v. 146, 2019, p. 713-721.https://doi.org/10.1016/j.microc.2019.01.073

ORDOÑEZ-FERNÁNDEZ, ZULMA-KATERINE; MONTOYA-BONILLA, BIBIANA-PATRICIA. Evaluación agronómica de Coffea Arábica variedad castillo y caturra en dos sistemas de producción (sol y sombra); en la hacienda los naranjos, vereda la Venta (Cajibio-Cauca). Revista de la Asociación Colombiana de Ciencias Biológicas, v. 1, n. 29, 2017, p. 58-66.https://revistaaccb.org/r/index.php/accb/article/view/141

PARDO-ESCOBAR, OSCAR-FRADIQUE. Respuestas espectrales a la fertilización Con nitrógeno y potasio en el cultivo del Banano (Musa aaa Simmonds), caso Municipio Zona Bananera [M.Sc. Thesis Geomatics]. Bogotá (Colombia): Universidad Nacional de Colombia, Facultad de Ciencias Agrarias, Escuela de Posgrados. 2015, 99 p.

PAREDES, JUAN-AUGUSTO; GONZALEZ, JESSENIA; SAITO, CARLOS; FLORES, ANDRES. Multispectral imaging system with UAV integration capabilities for crop analysis. Memorias First IEEE International Symposium of Geoscience and Remote Sensing (GRSS-CHILE). Valdivia (Chile): 2017, p. 1-4.https://doi.org/10.1109/GRSS-CHILE.2017.7996009

QIU, CHUNRONG; LIAO, GUIPING; TANG, HONGYUAN; LIU, FAN; LIAO, XIAOYI; ZHANG, RUI; ZHAO, ZANZHONG. Derivative Parameters of Hyperspectral NDVI and Its Application in the Inversion of Rapeseed Leaf Area Index. Applied Sciences, v. 8, n. 8, 2018, p. 1300.https://doi.org/10.3390/app8081300

RANI, MEENU; KUMAR, PAVAN; CHANDRA-PANDEY, PREM; SRIVASTAVA, PRASHANT K.; CHAUDHARY, B.S.; TOMAR, VANDANA; PRASAD-MANDAL, VINAY. Multi-temporal NDVI and surface temperature analysis for Urban Heat Island inbuilt surrounding of sub-humid region: A case study of two geographical regions. Remote Sensing Applications: Society and Environment, v. 10, 2018, p. 163-172.https://doi.org/10.1016/j.rsase.2018.03.007

RANJAN, RAKESH; CHANDEL, ABHILASH; KHOT, LAV; BAHLOL, HAITHAM; ZHOU, JIANFENG; BOYDSTON, RICK; MIKLAS, PHILLIP. Irrigated pinto bean crop stress and yield assessment using ground based low altitude remote sensing technology. Information Processing in Agriculture, v. 6, n. 4, 2019, p. 502-514.https://doi.org/10.1016/j.inpa.2019.01.005

SONG, HYEON-GI; BYEON, SEON-YEONG; CHUNG, GOO-YONG; JUNG, SANG-MYUNG; CHOI, JUNG-IL; SHIN, HWA-SUNG. A systematic correlation analysis of carotenoids, chlorophyll, non-pigmented cell mass, and cell number for the blueprint of Dunaliella salina culture in a photobioreactor. Bioprocess and Biosystems Engineering, v. 41, n. 9, 2018, p. 1295-1303.https://doi.org/10.1007/s00449-018-1957-5

VAN BREUSEGEM, FRANK; DAT, JAMES F. Reactive oxygen species in plant cell death. Plant physiology, v. 141, n. 2, 2006, p. 384–390.https://doi.org/10.1104/pp.106.078295

WIDJAJA-PUTRA, BAYU-TARUNA; SONI, PEEYUSH. Dataset of chlorophyll content estimation of Coffea canephora using Red and Near-Infrared consumer-grade camera. Data in Brief, v. 21, 2018, p. 736-741.https://doi.org/10.1016/j.dib.2018.10.035

ZHENG, HENGBIAO; CHENG, TAO; LI, DONG; ZHOU, XIANG; YAO, XIA; TIAN, YONGCHAO; CAO, WEIXING; ZHU, YAN. Evaluation of RGB, Color-Infrared and Multispectral Images Acquired from Unmanned Aerial Systems for the Estimation of Nitrogen Accumulation in Rice. Remote Sensing, v. 10, n. 6, 2018, p. 824.https://doi.org/10.3390/rs10060824

Cómo citar
Solis, A. F., Revelo luna , D. A., Campo Ceballos , D. A. ., & Gaviria López , C. A. (2021). Correlación del contenido de clorofila foliar de la especie Coffea arabica con índices espectrales en imágenes. Biotecnología En El Sector Agropecuario Y Agroindustrial, 19(2), 51–68. https://doi.org/10.18684/bsaa.v19.n2.2021.1536
Publicado
2021-01-23
Sección
Artículos de Investigaciòn