Use of multispectral images to evaluate the efficacy of pre-emergent herbicides in peas under greenhouse conditions

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Autores

David Alejandro Jamaica-Tenjo http://orcid.org/0000-0003-4331-8716
Andrés Esteban Puerto-Lara https://orcid.org/0000-0002-3818-5667
Jhonatan Javier Guerrero-Aldana https://orcid.org/0000-0001-7814-0044
Oscar Leonardo García-Navarrete https://orcid.org/0000-0001-5075-460X
Gustavo Adolfo Ligarreto-Moreno https://orcid.org/0000-0001-9372-6094

Abstract

In Colombia, peas are the second most important legume after the bean, and weeds are the main biotic factor that limits production, causing losses of up to 100%. Manual control can represent up to 40% of the labor force. The critical period in the crop-weed competition is the first third of the crop cycle; therefore, pre-emergent herbicide applications are a cost-effective way to control weeds. Common variables for assessing weed-control efficacy include, weed density (individuals/area), which is precise but time consuming, and weed coverage (%), which is faster but very subjective. Therefore, pre-emergence herbicides and a weed-control evaluation method that standardizes, facilitates, and provides greater precision are needed for peas cultivation and experimentation. Five pre-emergent herbicides (linuron, S-metolachlor, metribuzine, oxifluorfen and pendimetalin) were evaluated at two doses in a greenhouse pea crop. Also, two methods (quantification process of multispectral images and conventional human visual) for assessing weed coverage and control efficacy were compared. The best herbicide treatment for the dry grain yield was metribuzine (2.36 t ha-1). Furthermore, the effectiveness of the weed control was 88% at 36 days after sowing, which is optimal. Finally, there was agreement between the weed assessment methods (human vs. machine). The intraclass correlation coefficient was over 0.95, which validates the use of machine quantification for weed coverage.

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References

Agronet. 2018. Evaluaciones agropecuarias municipales. In: https://www.agronet.gov.co/estadistica/Paginas/home.aspx?cod=1#; consulted: October, 2020.

ANDI, Asociación Nacional de Empresarios de Colombia; ICA, Instituto Colombiano Agropecuario. 2015. Manual para elaboración de protocolos para ensayos de eficacia con PQUA. In: https://www.ica.gov.co/areas/agricola/servicios/regulacion-y-control-de-plaguicidas-quimicos/manual-protocolos-ensayos-eficacia-pqua-1.aspx; consulted: October, 2020.

Andújar, D., A. Ribeiro, R. Carmona, C. Fernández-Quintanilla, and J. Dorado. 2010. An assessment of the accuracy and consistency of human perception of weed cover. Weed Res. 50(6), 638-647. Doi: 10.1111/j.1365-3180.2010.00809.x

Bai, X., X. Li, Z. Fu, X. Lv, and L. Zhang. 2017. A fuzzy clustering segmentation method based on neighborhood grayscale information for defining cucumber leaf spot disease images. Comput. Electron. Agric. 136, 157-165. Doi: 10.1016/j.compag.2017.03.004

Banga, R.S., A. Yadav, R.S. Malik, and R.K. Malik. 1998. Evaluation of different herbicides for weed control in pea. Indian J. Weed Sci. 30(3-4), 145-148.

Benlloch, J., A. Sánchez-Salmerón, S. Christensen, and M. Walter. 1996. Weed mapping in cereal crops using image analysis techniques. pp. 1059-1060. In: Proc. 3rd International Conference on Precision Agriculture. Minneapolis, MN.

Blanco, Y. and A. Leiva. 2010. Abundancia y diversidad de especies de arvenses en el cultivo de maíz (Zea mays, L.) precedido de un barbecho transitorio después de la papa (Solanum tuberosum L.). Cultivos Tropicales 31(2), 12-16.

Bretag, T.W., P.J. Keane, and T.V. Price. 2006. The epidemiology and control of ascochyta blight in field peas: a review. Aust. J. Agric. Res. 57(8), 883. Doi: 10.1071/AR05222

CAN, Comisión de la Comunidad Andina. 1998. Decisión 436, Norma Andina para el Registro y Control de Plaguicidas Químicos de Uso Agrícola. GO 347. Cartagena, Colombia.

Castillejo-González, I., J.M. Peña-Barragán, M. Jurado-Expósito, F. Mesas-Carrascosa, and López-Granados. 2014. Evaluation of pixel- and object-based approaches for mapping wild oat (Avena sterilis) weed patches in wheat fields using QuickBird imagery for site-specific management. Eur. J. Agron. 59, 57-66. Doi: 10.1016/j.eja.2014.05.009

Ciancio, A. and K.G. Mukerji (eds.). 2007. General concepts in integrated pest and disease management. Springer, Dordrecht, The Netherlands.

DANE, Departamento Administrativo Nacional de Estadística of Colombia. 2015. Insumos y factores asociados a la producción agropecuaria: el cultivo de arveja. Boletín Mensual 33. Bogota.

Díaz, J. and M. Zapata. 1990. Control de malezas: práctica agronómica fundamental en el cultivo de la arveja. Investigación y Progreso Agropecuario Carillanca 9(4), 28-33.

Espinoza, N. and J. Ormeño. 1989. Las malezas en arveja y su control. Serie Carillanca. INIA. Temuco, Chile.

FAO. 2019. FAOSTAT – Data Crops In: http://www.fao.org/faostat/en/#data/QC; consulted: May, 2021.

Fenalce, Federación Nacional de Cultivadores de Cereales, Leguminosas y Soya of Colombia. 2010. El cultivo de la arveja, historia e importancia. Bogota.

Gamer, M., J. Lemon, I. Fellows, and P. Singh. 2010. Various coefficients of interrater reliability and agreement. Version 0.84.1. In: https://rdrr.io/cran/irr/; consulted: October, 2020.

Giavarina, D. 2015. Understanding bland altman analysis. Biochem. Med. 25(2), 141-151. Doi: 10.11613/BM.2015.015

González-Andújar, J.L., C. Fernández-Quintanilla, F. Bastida, R. Calvo, J. Izquierdo, and J. Lezaun. 2011. Assessment of a decision support system for chemical control of annual ryegrass (Lolium rigidum) in winter cereals. Weed Res. 51(3), 304-309. Doi: 10.1111/j.1365-3180.2011.00842.x

ICA, Instituto Colombiano Agropecuario. 2012. Manejo fitosanitario del cultivo de hortalizas - Medidas para la temporada invernal. Bogota.

ICA, Instituto Colombiano Agropecuario. 2019. Registros nacionales PQUA – Palguicidas registrados 2019. Retrieved from https://www.ica.gov.co/; consulted: October, 2020.

Jamaica, D. and G. Plaza. 2014. Evaluation of various conventional methods for sampling weeds in potato and spinach crops. Agron. Colomb. 32(1), 36-43. 10.15446/agron.colomb.v32n1.39613

Jurado-Expósito, M., F. López-Granados, S. Atenciano, L. Garcı́a-Torres, and J.L. González-Andújar. 2003. Discrimination of weed seedlings, wheat (Triticum aestivum) stubble and sunflower (Helianthus annuus) by near-infrared reflectance spectroscopy (NIRS). Crop Prot. 22(10), 1177-1180. Doi: 10.1016/S0261-2194(03)00159-5

Lescano, M.C., D. Faccini, E. Puricelli, and A. Nicolari. 2017. Evaluación de la eficacia de distintos herbicidas preemergentes selectivos para cultivos de soja y maíz en Chloris virgata. Agromensajes (August), 5-7.

Mawalia, A.K., S. Kumar, and S.S. Rana. 2016. Herbicide combinations for control of complex weed flora in garden pea. Indian J. Weed Sci. 48(1), 48-52. Doi: 10.5958/0974-8164.2016.00011.3

Nkoa, R., M.D.K. Owen, and C.J. Swanton. 2015. Weed abundance, distribution, diversity, and community analyses. Weed Sci. 63(Sp1), 64-90. Doi: 10.1614/WS-D-13-00075.1

Osorio, K., A. Puerto, C. Pedraza, D. Jamaica, and L. Rodríguez. 2020. A deep learning approach for weed detection in lettuce crops using multispectral images. AgriEngineering 2(3), 471-488. Doi: 10.3390/agriengineering2030032

Puerto, A. 2018. Clasificación y cuantificación de maleza en cultivos de hortalizas por medio de procesamiento de imágenes digitales multiespectrales. MSc thesis. Faculty of Engineering. Universidad Nacional de Colombia, Bogota.

Rana, S. 2002. Integrated weed management in pea (Pisum sativum L.) under Sangla valley conditions of Himachal Pradesh. Indian J. Weed Sci. 34(3-4), 204-207.

Semidey, N. and L. Almodóvar. 1987. Oxyfluorfen: A candidate herbicide for weed control in pigeon peas. J. Agr. U. Puerto Rico 71(3), 277-285.

Sobrino, J.A. 2000. Teledetección. Servicio de publiaciones-Universitat de València, Valencia, Spain.

Torres-Sánchez, J., F. López-Granados, and J.M. Peña. 2015. An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops. Comput. Electron. Agric. 114, 43-52. Doi: 10.1016/j.compag.2015.03.019

Wágner, G. and E. Nádasy. 2006. Effect of pre-emergence herbicides on growth parameters of green pea. Commun. Agric. Appl. Biol. Sci. 71(3 Pt A), 809-813.

Weis, M. and M. Sökefeld. 2010. Detection and identification of weeds. pp. 119-134. In: Oerke, E.C., R. Gerhards, G. Menz, and R. Sikora (eds.). Precision crop protection: the challenge and use of heterogeneity. Springer, Dordrecht, The Netherlands. Doi: 10.1007/978-90-481-9277-9_8

Yanniccari, M.E., C.M. Appella, and C.M. Istilart. 2017. Evaluación de tratamientos pre-emergentes para el control de malezas en el cultivo de arveja. Actualización Técnica de Cultivos 5(1), 108-109.

Zamorano, C., H. López, and G. Alzate. 2008. Evaluación de la competencia de arvenses en el cultivo de arveja (Pisum sativum) en Fusagasugá, Cundinamarca (Colombia). Agron. Colomb. 26(3), 443-450.

Zimdahl, R. 2018. Fundamentals of weed science. 5th ed. Academic Press, Burlington, MA. Doi: 10.1016/B978-0-12-811143-7.01001-5

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