The present work investigated the efficiency of leaf reflectance indices in the identification of Capsicum annuum L. var. annuum resistant to anthracnose in the fruit. Twenty-five F5:6 families originating from contrasting parents were assessed; the parents were accession UENF 2285 (susceptible to anthracnose) and accession UENF 1381, a hot pepper resistant to anthracnose in the fruit. The experiment was carried out in an experimental field in Campos dos Goytacazes, Rio de Janeiro, Brazil, between May and October of 2021. The treatments were arranged in a randomized block design, with three replications and five plants per plot. Fifteen LRIs were estimated using a CI-710 portable mini leaf spectrometer. The assessments covered all plant growth after flowering, and a total of six assessments were performed at 15-days intervals, beginning at 35 and ending 120 days after flowering (DAFs). Analysis of variance in a split-plot scheme was performed, as were tests of mean groupings and principal components analysis (PCA). The best period for evaluating leaf reflectance indices in C. annuum var. annuum is 120 days after flowering. The leaf reflectance indices PRI, CNDVI and Ctr2 stood out as effective in distinguishing between resistant and susceptible genotypes.
Garriga, M., Romero-Bravo, S., Estrada, F., Escobar, A., Matus, I.A., Del Pozo, A., Astudillo, C.A. and Lobos, G.A. (2017) Assessing Wheat Traits by Spectral Reflectance: Do We Really Need to Focus on Predicted Trait-Values or Directly Identify the Elite Genotypes Group? Frontiers in Plant Science, 8, Article 280. https://doi.org/10.3389/fpls.2017.00280
[3]
Araus, J.L. and Kefauver, S.C. (2018) Breeding to Adapt Agriculture to Climate Change: Affordable Phenotyping Solutions. Current Opinion in Plant Biology, 45, 237-247. https://doi.org/10.1016/j.pbi.2018.05.003
[4]
Borém, A., Miranda, G.V. and Fritsche-Neto, R. (2017) Plant Breeding/Melhoramento de Plantas, UFV, Viçosa, 93.
[5]
Araus, J.L. and Cairns, J.E. (2014) Field High-Throughput Phenotyping: The New Crop Breeding Frontier. Trends in Plant Science, 19, 52-61. https://doi.org/10.1016/j.tplants.2013.09.008
[6]
Devadas, R., Lamb, D.W., Simpfendorfer, S. and Backhouse, D. (2009) Evaluating Ten Spectral Vegetation Indices for Identifying Rust Infection in Individual Wheat Leaves. Precision Agriculture, 10, 459-470. https://doi.org/10.1007/s11119-008-9100-2
[7]
Blackburn, G.A. and Steele, C.M. (1999) Towards the Remote Sensing of Matorral Vegetation Physiology: Relationships between Spectral Reflectance, Pigment, and Biophysical Characteristics of Semiarid Bushland Canopies. Remote Sensing of Environment, 70, 278-292. https://doi.org/10.1016/S0034-4257(99)00044-9
[8]
Gitelson, A.A., Zur, Y., Chivkunova, O.B. and Merzlyak, M.N. (2002) Assessing Carotenoid Content in Plant Leaves with Reflectance Spectroscopy. Photochemistry and Photobiology, 75, 272-281. https://doi.org/10.1562/0031-8655(2002)075<0272:ACCIPL>2.0.CO;2
[9]
Jacquemoud, S. and Baret, F. (1990) Prospect: A Model of Leaf Optical Properties Spectra. Remote Sensing of Environment, 34, 75-91. https://doi.org/10.1016/0034-4257(90)90100-Z
[10]
Natarajan, S., Basnayake, J., Wei, X. and Lakshmanan, P. (2019) High-Throughput Phenotyping of Indirect Traits for Early-Stage Selection in Sugarcane Breeding. Remote Sensing, 11, Article 2952. https://doi.org/10.3390/rs11242952
[11]
Lozada, D.N., Godoy, J.V., Ward, B.P. and Carter, A.H. (2020) Genomic Prediction and Indirect Selection for Grain Yield in US Pacific Northwest Winter Wheat Using Spectral Reflectance Indices from High-Throughput Phenotyping. International Journal of Molecular Sciences, 21, Article 165. https://doi.org/10.3390/ijms21010165
[12]
Heim, R.H.J., Wright, I.J., Allen, A.P., Geedicke, I. and Oldeland, J. (2019) Developing a Spectral Disease Index for Myrtle Rust (Austropuccinia psidii). Plant Pathology, 68, 738-745. https://doi.org/10.1111/ppa.12996
[13]
Kalaivani, S., Shantharajah, S.P. and Padma, T. (2020) Agricultural Leaf Blight Disease Segmentation Using Indices Based Histogram Intensity Segmentation Approach. Multimedia Tools and Applications, 79, 9145-9159. https://doi.org/10.1007/s11042-018-7126-7
[14]
de Almeida, C.L.P., Bento, C.S., Sudré, C.P., Pimenta, S., Gonçalvez, L.S.A. and Rodrigues, R. (2020) Genotype-Ideotype Distance Index and Multivariate Analysis to Select Sources of Anthracnose Resistance in Capsicum spp. European Journal Plant Pathology, 156, 223-236. https://doi.org/10.1007/s10658-019-01879-9
[15]
Giacomin, R.M., Ruas, C.F., Moreira, A.F.P., Guidone, G.H.M., Baba, V.Y., Rodrigues, R. and Gonçalves, L.S.A. (2020) Inheritance of Anthracnose Resistance (Colletotrichum scovillei) in Ripe and Unripe Capsicum Annuum Fruits. Journal of Phytopathology, 168, 184-192. https://doi.org/10.1111/jph.12880
[16]
Peñuelas, J., Gamon, J.A., Fredeen, A.L., Merino, J. and Field, C.B. (1994) Reflectance Indices Associated with Physiological Changes in Nitrogen and Water Imited Sunflower Leaves. Remote Sensing of Environment, 48, 135-146. https://doi.org/10.1016/0034-4257(94)90136-8
[17]
Zhao, D., Reddy, R.K., Kakani, V.G., Read, J.J. and Carter, G.A. (2003) Corn (Zea mays L.) Growth, Leaf Pigment Concentration, Photosynthesis and Leaf Hyperspectral Reflectance Properties as Affected by Nitrogen Supply. Plant and Soil, 257, 205-217. https://doi.org/10.1023/A:1026233732507
[18]
Dallagnol, L.J. (2018) Genetic Resistance of Plants to Pathogens/Resistência Genetica de Plantas a Patógenos, UFPel, E., Pelotas, 13-64.
[19]
Bento, C.S., Souza, A.G., Sudré, C.P., Pimenta, S. and Rodrigues, R. (2017) Multiple Genetic Resistances in Capsicum Spp. Genetics and Molecular Research, 16, gmr16039789. https://doi.org/10.4238/gmr16039789
[20]
de Meterologia, I.N. (2023) INMET. https://portal.inmet.gov.br/#avisos
[21]
Nick, C. and Borém, A. (2016) Peppers: From Planting to Harvesting/Pimentão: Do Plantio à Colheita, UFV, Viçosa, 204.
[22]
Silva, J.R.A., Chaves, T.P., Silva, A.R.G., Barbosa, L.F., Costa, J.F.O. and Assuncao, I.P. (2017) Molecular and Morpho-Cultural Characterization of Colletotrichum spp. Associated with Anthracnose on Capsicum spp. in Northeastern Brazil. Tropical Plant Pathology, 42, 315-319. https://doi.org/10.1007/s40858-017-0151-7
[23]
Geronimo, I.G.C. (2018) Melhoramento de Capsicum annuum Visando Resistência à Antracnose: Caracterização de Isolados de Colletotrichum spp., Herança da Resistência e Proposta de Série Diferenciadora. Ph.D Thesis, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes.
[24]
Araújo, M.S.B. (2020) Associação do Método Genealógico e Modelagem Mista no Melhoramento de Capsicum annuum var. annuum Para Resistência à Antracnose e Atributos Produtivos e Mapeamento de Novos Genes de Resistência à Mancha Bacteriana. Ph.D Thesis, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes.
[25]
Alencar, A.A.S. (2022) Interação Planta-Patógeno em Capsicum annuum var. annuum: Melhoramento Genético Visando à Resistência à Antracnose (Colletotrichum scovillei) e Identificação de Proteínas Relacionadas à Defesa no Patossistema Capsicum-Xanthomonas. Ph.D Thesis, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes.
[26]
Janeczko, A., Dziurka, M., Gullner, G., Kocurek, M., Rys, M., Saja, D., Skoczowski, A., Tóbiás, I., Kornas, A. and Barna, B. (2018) Comparative Studies of Compatible and Incompatible Pepper—Tobamovirus Interactions and The Evaluation of Effects of 24-Epibrassinolide. Photosynthetica, 56, 763-775. https://doi.org/10.1007/s11099-017-0725-0
[27]
Rouse, J., Haas, R.H., Schell, J.A. and Deering, D.W. (1974) Monitoring Vegetation Systems in The Great Plains with ERTS. NASA. Goddard Space Flight Center 3d ERTS-1 Symposium, 1, 309-317.
[28]
Haboudane, D., Miller, J.R., Tremblay, N., Zarco-Tejada, P.J. and Dextraze, L. (2002) Integrated Narrow-Band Vegetation Indices for Prediction of Crop Chlorophyll Content for Application to Precision Agriculture. Remote Sensing of Environment, 81, 416-426. https://doi.org/10.1016/S0034-4257(02)00018-4
[29]
Genovese, G., Vignolles, C., Nègre, T. and Passera, G. (2001) A Methodology for a Combined Use of Normalised Difference Vegetation Index and CORINE Land Cover Data for Crop Yield Monitoring and Forecasting. A Case Study on Spain. Agronomie, 21, 91-111. https://doi.org/10.1051/agro:2001111
[30]
Gamon, J.A., Serrano, L. and Surfus, J.S. (1997) The Photochemical Reflectance Index: An Optical Indicator of Photosynthetic Radiation Use Efficiency across Species, Functional Types, and Nutrient Levels. Oecologia, 112, 492-501. https://doi.org/10.1007/s004420050337
[31]
Gitelson, A.A., Merzlyak, M.N. and Chivkunova, O.B. (2001) Optical Properties and Nondestructive Estimation of Anthocyanin Content in Plant Leaves. Photochemistry and Photobiology, 74, 38-45. https://doi.org/10.1562/0031-8655(2001)074<0038:OPANEO>2.0.CO;2
[32]
Gitelson, A.A., Zur, Y., Chivkunova, O.B. and Merzlyak, M.N. (2002) Assessing Carotenoid Content in Plant Leaves with Reflectance Spectroscopy. Photochemistry and photobiology, 75, 272-281. https://doi.org/10.1562/0031-8655(2002)075<0272:ACCIPL>2.0.CO;2
[33]
Penuelas, J., Filella, I., Biel, C., Serrano, L. and Save, R. (1993) The Reflectance at the 950-970 nm Region as an Indicator of Plant Water Status. International Journal of Remote Sensing, 14, 1887-1905. https://doi.org/10.1080/01431169308954010
[34]
Peñuelas, J., Filella, I. and Gamon, J.A. (1995) Assessment of Photosynthetic Radiation‐Use Efficiency with Spectral Reflectance. New Phytologist, 131, 291-296. https://doi.org/10.1111/j.1469-8137.1995.tb03064.x
[35]
Carter, G.A. and Miller, R.L. (1994) Early Detection of Plant Stress by Digital Imaging within Narrow Stress-Sensitive Wavebands. Remote Sensing of Environment, 50, 295-302. https://doi.org/10.1016/0034-4257(94)90079-5
[36]
Merzlyak, M.N., Solovchenko, A.E., I., Smagin, A. and Gitelson, A.A. (2005) Apple Flavonols during Fruit Adaptation to Solar Radiation: Spectral Features and Technique for Non-Destructive Assessment. Journal of plant physiology, 162, 151-160. https://doi.org/10.1016/j.jplph.2004.07.002
[37]
Cruz, C.D. (2013) GENES: A Software Package for Analysis in Experimental Statistics and Quantitative Genetics. Acta Scientiarum. Agronomy, 35, 271-276.
[38]
Grange, A. (2009) BiplotGUI: Interactive Biplots in R. Journal of Statistical Software, 30, 1-37. https://doi.org/10.18637/jss.v030.i12
[39]
Jollife, I.T. and Cadima, J. (2016) Principal Component Analysis: A Review and Recent Developments. Philosophical Transactions of the Royal Society A, 374, 1-16. https://doi.org/10.1098/rsta.2015.0202
[40]
Wickham, H. (2016) ggplot2: Elegant Graphics for Data Analysis. Verlag, New York. https://ggplot2.tidyverse.org/ https://doi.org/10.1007/978-3-319-24277-4
Bai, G., Ge, Y., Hussain, W., Baenziger, P.S. and Graef, G. (2016) A Multi-Sensor System for High Throughput Field Phenotyping in Soybean and Wheat Breeding. Computers and Electronics in Agriculture, 128, 181-192. https://doi.org/10.1016/j.compag.2016.08.021
[43]
Saja, D., Janeczko, A., Barna, B., Skoczowski, A., Dziurka, M., Kornaś, A. and Gullner, G. (2020) Powdery Mildew-Induced Hormonal and Photosynthetic Changes in Barley Near Isogenic Lines Carrying Various Resistant Genes. International Journal of Molecular Sciences, 21, Article 4536. https://doi.org/10.3390/ijms21124536
[44]
Liu, R., Song, Y., Liu, Y., Li, X., Song, H., Sun, C. and Wang, L. (2021) Changes in the Tree-Ring Width-Derived Cumulative Normalized Difference Vegetation Index over Northeast China During 1825 to 2013 CE. Forests, 12, Article 241. https://doi.org/10.3390/f12020241
[45]
Mahlein, A.K., Rumpf, T., Welke, P., Dehne, H.W., Plümer, L., Steiner, U. and Oerke, E.C. (2013). Development of Spectral Indices for Detecting and Identifying Plant Diseases. Remote Sensing of Environment, 128, 21-30. https://doi.org/10.1016/j.rse.2012.09.019
[46]
Machado, M.L., Pinto, F.A., de Queiroz, D.M., de Paula Jr., T.J. and Vieira, R.F. (2015) Estimative of White Mold Severity in Common Bean Crops Using Hyper and Multispectral Sensors/Estimativa de severidade do mofo-branco em lavouras de feijao utilizando-se sensores hiper e multiespectral. Revista Brasileira de Engenharia Agrícola e Ambiental, 19, 426-433. https://doi.org/10.1590/1807-1929/agriambi.v19n5p426-432
[47]
Sharifi, A. (2020) Remotely Sensed Vegetation Indices for Crop Nutrition Mapping. Journal of the Science of Food and Agriculture, 100, 5191-5196. https://doi.org/10.1002/jsfa.10568
[48]
Ramaroson, M.L., Koutouan, C., Helesbeux, J.J., Le Clerc, V., Hamama, L., Geoffriau, E. and Briard, M. (2022) Role of Phenylpropanoids and Flavonoids in Plant Resistance to Pests and Diseases. Molecules, 27, Article 8371. https://doi.org/10.3390/molecules27238371
[49]
Treutter, D. (2006) Significance of Flavonoids in Plant Resistance: A Review. Environmental Chemistry Letters, 4, 147-157. https://doi.org/10.1007/s10311-006-0068-8
[50]
Schlösser, E. (1993) Preformed Phenols as Resistance Factors. In: Geibel, M., Treutter, D. and Feucht, W., Eds., International Symposium on Natural Phenols in Plant Resistance, International Society for Horticultural Science, Weihenstephan, 615-630. https://doi.org/10.17660/ActaHortic.1994.381.85
