Spectral curves for identification of weeds in wheat crop

Autores

DOI:

https://doi.org/10.13083/reveng.v28i.8154

Palavras-chave:

reflectance, spectral bands, wavelengths

Resumo

The principal weeds in wheat cultivation are black oats and ryegrass and their control is generally performed without considering the spatial variability of the density of weed infestation. One way to identify weed species is by analyzing spectral curves of the targets. The objective of this work was to evaluate the spectral curves of wheat, black oats and ryegrass to identify which wavelengths are able to distinguish these species. The experiment was set using the species: black oats, ryegrass and wheat. Each species was sown in individual experimental plots in a completely randomized design with nine replications. HandHeld 2, ASD® spectroradiometer with 325-1075 nm spectral range was used to perform readings at full bloom stage. Then, the reflectance spectral data were grouped into eight spectral bands: violet, blue, green, yellow, orange, red, red edge and near infrared. Descriptive statistics of reflectance of the targets as well as analysis of variance (p<0.05) and test of Tukey for comparison of the means (p<0.01) were performed using the reflectance measurement of each spectral band. The results showed that the yellow and orange spectral bands obtained higher capacities of differentiation of the species under study. It can be concluded that the analysis of spectral curves of target of black oat and ryegrass weeds and wheat crop makes it possible to differentiate species in full bloom stage.

Downloads

Não há dados estatísticos.

Biografia do Autor

Luan Pierre Pott, Universidade Federal de Santa MariaKansas State University

Mestrando em Engenharia Agrícola na Universidade Federal de Santa Maria, com período sandwich em Kansas State University

Telmo Jorge Carneiro Amado

Professor Titular UFSM, PPG Mestrado em Agricultura de Precisão e Engenharia Agrícola

Elodio Sebem

Professor UFSM, PPG Mestrado em Agricultura de Precisão

Raí Augusto Schwalbert, UFSM

Doutorando em Engenharia Agrícola UFSM

Referências

ALVARES, C.A.; STAPE, J.L.; SENTELHAS, P.C.; DE MORAES, J.L.G.; SPAROVEK G. Koppen’s climate classification map for Brazil. Meteorologische Zeitschrift, Schweizerbart, v.22, n.6, p.711–728, 2013.

DASS A.; SHEKHAWAT, K.; CHOUDHARY, A.K.; SEPAT, S.; RATHORE, S.S.; MAHAJAN, G.; CHAUHAN, B.S. Weed management in rice using crop competition-a review. Crop Protection, United States, v.95, p.45–52, 2017.

FEILHAUER, H.; SOMERS, B.; VAN DER LINDEN, S. Optical trait indicators for remote sensing of plant species composition: Predictive power and seasonal variability. Ecological Indicators, v.73, 825-33, 2017.

GAO, J.; NUYTTENS, D.; LOOTENS, P.; HE, Y.; PIETERS, J.G. Recognising weeds in a maize crop using a random forest machine-learning algorithm and near-infrared snapshot mosaic hyperspectral imagery. Biosystem Engineering, v.170, p.39–50, 2018.

HEAP, I. The International Survey of Herbicide Resistant Weeds. 2019. Online. Internet. Acessado em: 23/08/2019. Disponível em: www.weedscience.org

HERRMANN, I.; SHAPIRA, U.; KINAST, S.; KARNIELI, A.; BONFIL, D.J. Ground-level hyperspectral imagery for detecting weeds in wheat fields. Precision Agriculture, United States, v.14, n.6, p.637–659, 2013.

HUANG, H.; DENG, J.; LAN, Y.; YANG, A.; DENG, X.; WEN, S.; ZHANG, H.; ZHANG, Y. Accurate Weed Mapping and Prescription Map Generation Based on Fully Convolutional Networks Using UAV Imagery. Sensors, United States, v.18, n.10, p.3299-3311, 2018.

HUANG, Y.; LEE, M.A.; THOMSON, S.J.; REDDY, K.N. Ground-based hyperspectral remote sensing for weed management in crop production. International Journal of Agricultural and Biological Engineering, United States, v.9, n.2, p.98-109, 2016.

LAMEGO, F.P.; RUCHEL, Q.; KASPARY, T.E.; GALLON, M.; BASSO, C.J.; SANTI, A.L. Habilidade competitiva de cultivares de trigo com plantas daninhas. Planta Daninha, Viçosa, v.31, n.3, p.521–531, 2013.

LIPPERT, D.B.; BENEDETTI, A.C.P.; MUNIZ, M.F.B.; PEREIRA, R.S.; JUNIOR, C.A.B.; FINKENAUER, E.; BERRA, E.F. Comportamento espectral de folhas de Eucalyptus globulus (Labill.) atacadas por Mycosphaerella spp. nas regiões do visível e do infravermelho próximo do espectro eletromagnético. Ciência Florestal, Santa Maria, v.25, n.1, p.211-219, 2015.

LÓPEZ-GRANADOS, F.; TORRES-SÁNCHEZ, J.; SERRANO-PÉREZ, A.; DE CASTRO, A.I.; CARRASCOSA, J.M.; PEÑA, J.M. Early season weed mapping in sunflower using UAV technology: Variability of herbicide treatment maps against weed thresholds. Precision Agriculture, United States, v.17, n.2, p.183–199, 2016.

LOUARGANT, M.; JONES, G.; FAROUX, R.; PAOLI, J.P.; MAILLOT, T.; GÉE, C.; VILLETTE, S. Unsupervised Classification Algorithm for Early Weed Detection in Row-Crops by Combining Spatial and Spectral Information. Remote Sensing, v.10, n.5, p.1-18, 2018.

MARSHALL, R.; MOSS, S.R. Characterisation and molecular basis of ALS inhibitor resistance in the grass weed Alopecurus myosuroides. Weed Research, United States, v.48, n.5, p.439–447, 2008.

MIRIK, M.; ANSLEY, R.J.; STEDDOM, K.; JONES, D.C.; RUSH, C.M.; MICHELS, G.J.; ELLIOTT, N.C. Remote distinction of a noxious weed (Musk Thistle: Carduus Nutans) using airborne hyperspectral imagery and the support vector machine classifier. Remote Sensing, Switzerland, v.5, n.2, p.612-630, 2013.

PEÑA, J.M.; TORRES-SÁNCHEZ, J.; DE CASTRO, A.I.; KELLY, M.; LÓPEZ-GRANADOS, F. Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images. PLoS ONE, v.8, n.10, e77151, 2013.

PEÑA, J.M.; TORRES-SÁNCHEZ, J.; SERRANO-PÉREZ, A.; DE CASTRO, A.I.; LÓPEZ-GRANADOS, F. Quantifying efficacy and limits of unmanned aerial vehicle (UAV) technology for weed seedling detection as affected by sensor resolution. Sensors, Switzerland, v.15, n.3, p.5609–5626, 2015.

PÉREZ-ORTIZ, M.; PEÑA, J.M.; GUTIÉRREZ, P.A.; TORRES-SÁNCHEZ, J.; MARTÍNEZ, C.H.; LÓPEZ-GRANADOS, F. Selecting patterns and features for between-and within-crop-row weed mapping using UAV-imagery. Expert System with Applications, v.47, p.85–94, 2016.

R CORE TEAM 2018. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (Acesso em 10 de abril 2018).

SANTOS, H.G.; JACOMINE, P.K.T.; ANJOS, L.H.C.; OLIVEIRA, V.A.; LUMBRERAS, J.F.; COELHO, M.R.; ALMEIDA, J.A.; CUNHA, T.J.B.; OLIVEIRA, J.B. Embrapa: Sistema Brasileiro de Classificação de Solos. 3ª ed Brasília. 353p, 2013.

SHAPIRA, U.; HERRMANN, I.; KARNIELI, A.; BONFIL, D.J. Field spectroscopy for weed detection in wheat and chickpea fields. Remote Sensing, Switzerland, v.34, n.17, p.6094–6108, 2013.

TORRES-SÁNCHEZ, J.; LÓPEZ-GRANADOS, F.; DE CASTRO, A.I.; PEÑA-BARRAGÁN, J.M. Configuration and specifications of an unmanned aerial vehicle (UAV) for early site-specific weed management. PLoS ONE, v.8, n.3, e58210, 2013.

Downloads

Publicado

2020-01-29

Como Citar

Pott, L. P., Amado, T. J. C., Sebem, E., & Schwalbert, R. A. (2020). Spectral curves for identification of weeds in wheat crop. Revista Engenharia Na Agricultura - Reveng, 28, 51-57. https://doi.org/10.13083/reveng.v28i.8154

Edição

Seção

Mecanização Agrícola

Artigos mais lidos pelo mesmo(s) autor(es)