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Detecting the attack of the fall armyworm (Spodoptera frugiperda) in cotton plants with machine learning and spectral measurements

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Abstract

The Spodoptera frugiperda (i.e., fall armyworm) causes irreversible damage in cotton cultivars, and its visual inspection on plants is a burdensome task for humans. A recent strategy to automatically do similar tasks is processing hyperspectral reflectance measurements with machine learning algorithms. Herein, its proposed a framework for modeling the spectral response of cotton plants under the fall armyworm attacks using machine learning algorithms, culminating in a theoretical model creation based on the band simulation process. A controlled experiment was conducted to collect hyperspectral radiance measurements from health and damage cotton plants over eight days. A hand-held spectroradiometer operating from 350 to 2500 nm was used. Several algorithms were evaluated, and a ranking approach was adopted to identify the most contributive wavelengths for detecting the damage. The Self-Organizing Map method was applied to organize the spectral wavelengths into groups, favoring the theoretical model creation for two sensors: OLI (Landsat-8) and MSI (Sentinel-2). It was found that the Random Forest algorithm produced the most suitable model, and the last day of analysis was better to separate healthy and damaged plants (F-measure: 0.912). The best spectral regions range from the red to near-infrared (650 to 1350 nm) and the shortwave infrared (1570 to 1640 nm). The theoretical model returned accurate results using both sensors (OLI, F-Measure = 0.865, and MSI, F-Measure = 0.886). In conclusion, the proposed framework contributes to accurately identifying cotton plants under the Spodoptera frugiperda attack for both hyperspectral and multispectral scales.

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Acknowledgements

The authors acknowledge the support of the UFMS (Federal University of Mato Grosso do Sul) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) (Finance Code 001).

Funding

This research was partially funded by National Council for Scientific and Technological Development (CNPq), project number: 433783/2018-4, 303559/2019-5, and 304052/2019-1, and received financial support from the Brazilian Corporation of Agricultural Research (EMBRAPA), project number: 11.14.09.001.04.00.

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Authors and Affiliations

  1. Environment and Regional Development Program, University of Western São Paulo, Rodovia Raposo Tavares, km 572-Limoeiro, Presidente Prudente, São Paulo, 19067-175, Brazil

    Ana Paula Marques Ramos, Felipe David Georges Gomes, Mayara Maezano Faita Pinheiro & Danielle Elis Garcia Furuya

  2. Faculty of Engineering, Architecture, and Urbanism and Geography, Federal University of Mato Grosso do Sul, Avenida Costa e Silva, Campo Grande, Mato Grosso do Sul, 79070-900, Brazil

    Wesley Nunes Gonçalvez & José Marcato Junior

  3. National Research Center of Development of Genetic Research and Biotechnology, Brazilian Agricultural Research Agency, Parque Estação Biológica W5 Asa Norte, Brasília, DF, 70770-917, Brazil

    Mirian Fernandes Furtado Michereff, Maria Carolina Blassioli-Moraes, Miguel Borges & Raúl Alberto Alaumann

  4. Forest Engineering Department, Santa Catarina State University, Av. Luiz de Camões, 2090, Conta Dinheiro, Lages, SC, 88520-000, Brazil

    Veraldo Liesenberg

  5. National Research Center of Development of Agricultural Instrumentation, Brazilian Agricultural Research Agency, Rua XV de Novembro, 1452, São Carlos, São Paulo, 13560-970, Brazil

    Lúcio André de Castro Jorge

  6. Agronomy Program, University of Western São Paulo, Rodovia Raposo Tavares, km 572-Limoeiro, Presidente Prudente, São Paulo, 19067-175, Brazil

    Ana Paula Marques Ramos

  7. Faculty of Engineering and Architecture and Urbanism, University of Western São Paulo, Rodovia Raposo Tavares, km 572-Limoeiro, Presidente Prudente, São Paulo, 19067-175, Brazil

    Lucas Prado Osco

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Ramos, A.P.M., Gomes, F.D.G., Pinheiro, M.M.F. et al. Detecting the attack of the fall armyworm (Spodoptera frugiperda) in cotton plants with machine learning and spectral measurements. Precision Agric 23, 470–491 (2022). https://doi.org/10.1007/s11119-021-09845-4

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