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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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).
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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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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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DOI: https://doi.org/10.1007/s11119-021-09845-4