Data/hora: 28/05/2024 14:16:07

Biblioteca(s): 	Embrapa Instrumentação; Embrapa Recursos Genéticos e Biotecnologia.	
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