Data/hora: 23/06/2024 10:07:30

Biblioteca(s): | Embrapa Instrumentação; Embrapa Recursos Genéticos e Biotecnologia.
Tipo da produção científica: | Artigo em Periódico Indexado
Circulação/Nível: | A - 1
Data corrente: | 10/09/2021
Data da última atualização: | 03/10/2023
Autoria: | RAMOS, A. P. M.; GOMES, F. D. G.; PINHEIRO, M. M. F.; FURUYA, D. E. G.; GONÇALVEZ, W. N.; MARCATO JUNIOR, J.; MICHEREFF, M. F. F.; MORAES, M. C. B.; BORGES, M.; LAUMANN, R. A.; LIESENBERG, V.; JORGE, L. A. de C.; OSCO, L. P.
Afiliação: | ANA PAULA MARQUES RAMOS, UNOESTE; FELIPE DAVID GEORGES GOMES, UNOESTE; MAYARA MAEZANO FAITA PINHEIRO, UNOESTE; DANIELLE ELIS GARCIA FURUYA, UNOESTE; WESLEY NUNES GONÇALVEZ, UFMS; JOSÉ MARCATO JUNIOR, UFMS; MIRIAN FERNANDES FURTADO MICHEREFF; MARIA CAROLINA BLASSIOLI MORAES, Cenargen; MIGUEL BORGES, Cenargen; RAUL ALBERTO LAUMANN, Cenargen; VERALDO LIESENBERG, Udesc; LUCIO ANDRE DE CASTRO JORGE, CNPDIA; LUCAS PRADO OSCO, UNOESTE.
Título: | Detecting the attack of the fall armyworm (Spodoptera frugiperda) in cotton plants with machine learning and spectral measurements.
Ano de publicação: | 2021
Fonte/Imprenta: | Precision Agriculture, 2021.
DOI: | https://doi.org/10.1007/s11119-021-09845-4
Idioma: | Inglês
Notas: | Na publicação: Maria Carolina Blassioli-Moraes; Raúl Alberto Alaumann.
Conteúdo: | 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.
Palavras-Chave: | Insect damage; Machine learning; Spectral data; Theoretical model.
Categoria do assunto: | --
Registro original: | Embrapa Instrumentação (CNPDIA)

Exemplares:

CENARGEN - (38616-1) - UPC - AP - DD -  -  -  - DIS
CNPDIA - (17921-1) - UPC - AP - DD - PROCI.21/164 -  - 2021/168 - DIS