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Registro Completo |
Biblioteca(s): |
Embrapa Instrumentação. |
Data corrente: |
16/11/2021 |
Data da última atualização: |
09/06/2022 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
FURUYA, D. E. G.; MA, L.; PINHEIRO, M. M. F.; GOMES, F. D. G.; GONÇALVEZ, W. N.; MARCATO JUNIOR, J.; RODRIGUES, D. de C.; BLASSIOLI- MORAES, M. C.; MICHEREFF, M. F. F.; BORGES, M.; ALAUMANN, R. A.; FERREIRA, E. J.; OSCO, L. P.; RAMOS, A. P. M.; LI, J.; JORGE, L. A. de C. |
Afiliação: |
MARIA CAROLINA BLASSIOLI MORAES, Cenargen; MIGUEL BORGES, Cenargen; EDNALDO JOSE FERREIRA, CNPDIA; LUCIO ANDRE DE CASTRO JORGE, CNPDIA. |
Título: |
Prediction of insect-herbivory-damage and insect-type attack in maize plants using hyperspectral data. |
Ano de publicação: |
2021 |
Fonte/Imprenta: |
International Journal of Applied Earth Observation and Geoinformation, v. 105, 102608, 2021. |
Páginas: |
1 - 10 |
ISSN: |
0303-2434 |
DOI: |
https://doi.org/10.1016/j.jag.2021.102608 |
Idioma: |
Inglês |
Conteúdo: |
Accurately detecting the insect damage caused in plants might reduce losses in crop yields. Hyperspectral data is a well-accepted data source to attend this issue. However, due to their high dimensional, both robust and intelligent methods are required to extract information from these datasets. Therefore, we explore the processing of hyperspectral data with artificial intelligence methods joined with clustering techniques to detect insect herbivory damage in maize plants. We measured the leaf spectral response from three different groups of maize plants: control (undamaged plants); damaged by Spodoptera frugiperda herbivory, and damaged by Dichelops meiacanthus. Data were collected with a FieldSpec 3.0 Spectroradiometer from 350 to 2500 nm for eight consecutive days. We adjusted eight machine learning methods. We also determined the most contributive wavelengths to differentiate undamaged from damaged plants by insect herbivore attack using clustering strategy. For that, we applied the clusterization method based on a self-organizing map (SOM). The Random Forest (RF) model is the overall best learner, and up to the 5th day of analysis represents the most adequate day to segregate maize undamaged from damaged maize. RF was able to separate the three groups of treatments with an F1-measure of up to 96.7% (Recall of 96.7% and Precision of 96.7%). Additionally, we found out that the most representative spectral regions are located in the near-infrared range. Our approach consists of an original contribution to early differentiate the undamaged plant from the damaged one due to insect-attack, highlighting the most contributive wavelengths to map this occurrence. MenosAccurately detecting the insect damage caused in plants might reduce losses in crop yields. Hyperspectral data is a well-accepted data source to attend this issue. However, due to their high dimensional, both robust and intelligent methods are required to extract information from these datasets. Therefore, we explore the processing of hyperspectral data with artificial intelligence methods joined with clustering techniques to detect insect herbivory damage in maize plants. We measured the leaf spectral response from three different groups of maize plants: control (undamaged plants); damaged by Spodoptera frugiperda herbivory, and damaged by Dichelops meiacanthus. Data were collected with a FieldSpec 3.0 Spectroradiometer from 350 to 2500 nm for eight consecutive days. We adjusted eight machine learning methods. We also determined the most contributive wavelengths to differentiate undamaged from damaged plants by insect herbivore attack using clustering strategy. For that, we applied the clusterization method based on a self-organizing map (SOM). The Random Forest (RF) model is the overall best learner, and up to the 5th day of analysis represents the most adequate day to segregate maize undamaged from damaged maize. RF was able to separate the three groups of treatments with an F1-measure of up to 96.7% (Recall of 96.7% and Precision of 96.7%). Additionally, we found out that the most representative spectral regions are located in the near-infrared range. Our approach consis... Mostrar Tudo |
Palavras-Chave: |
Proximal hyperspectral sensing; Random forest. |
Categoria do assunto: |
-- |
Marc: |
LEADER 02800naa a2200361 a 4500 001 2136152 005 2022-06-09 008 2021 bl uuuu u00u1 u #d 022 $a0303-2434 024 7 $ahttps://doi.org/10.1016/j.jag.2021.102608$2DOI 100 1 $aFURUYA, D. E. G. 245 $aPrediction of insect-herbivory-damage and insect-type attack in maize plants using hyperspectral data.$h[electronic resource] 260 $c2021 300 $a1 - 10 520 $aAccurately detecting the insect damage caused in plants might reduce losses in crop yields. Hyperspectral data is a well-accepted data source to attend this issue. However, due to their high dimensional, both robust and intelligent methods are required to extract information from these datasets. Therefore, we explore the processing of hyperspectral data with artificial intelligence methods joined with clustering techniques to detect insect herbivory damage in maize plants. We measured the leaf spectral response from three different groups of maize plants: control (undamaged plants); damaged by Spodoptera frugiperda herbivory, and damaged by Dichelops meiacanthus. Data were collected with a FieldSpec 3.0 Spectroradiometer from 350 to 2500 nm for eight consecutive days. We adjusted eight machine learning methods. We also determined the most contributive wavelengths to differentiate undamaged from damaged plants by insect herbivore attack using clustering strategy. For that, we applied the clusterization method based on a self-organizing map (SOM). The Random Forest (RF) model is the overall best learner, and up to the 5th day of analysis represents the most adequate day to segregate maize undamaged from damaged maize. RF was able to separate the three groups of treatments with an F1-measure of up to 96.7% (Recall of 96.7% and Precision of 96.7%). Additionally, we found out that the most representative spectral regions are located in the near-infrared range. Our approach consists of an original contribution to early differentiate the undamaged plant from the damaged one due to insect-attack, highlighting the most contributive wavelengths to map this occurrence. 653 $aProximal hyperspectral sensing 653 $aRandom forest 700 1 $aMA, L. 700 1 $aPINHEIRO, M. M. F. 700 1 $aGOMES, F. D. G. 700 1 $aGONÇALVEZ, W. N. 700 1 $aMARCATO JUNIOR, J. 700 1 $aRODRIGUES, D. de C. 700 1 $aBLASSIOLI- MORAES, M. C. 700 1 $aMICHEREFF, M. F. F. 700 1 $aBORGES, M. 700 1 $aALAUMANN, R. A. 700 1 $aFERREIRA, E. J. 700 1 $aOSCO, L. P. 700 1 $aRAMOS, A. P. M. 700 1 $aLI, J. 700 1 $aJORGE, L. A. de C. 773 $tInternational Journal of Applied Earth Observation and Geoinformation$gv. 105, 102608, 2021.
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Embrapa Instrumentação (CNPDIA) |
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Biblioteca(s): |
Embrapa Semiárido. |
Data corrente: |
12/11/1999 |
Data da última atualização: |
29/09/2023 |
Tipo da produção científica: |
Comunicado Técnico/Recomendações Técnicas |
Autoria: |
AMORIM, M. C. C. de; SILVA JUNIOR, L. G. de A.; PORTO, E. R. |
Afiliação: |
MIRIAM CLEIDE CAVALCANTE DE AMORIM, COMPESA; LUIZ GONZAGA DE ALBUQUERQUE SILVA JÚNIOR, IICA/CHESF; EVERALDO ROCHA PORTO, CPATSA. |
Título: |
Caracterização e prevenção de incrustações em sistemas de dessalinização por osmose inversa. |
Ano de publicação: |
1999 |
Fonte/Imprenta: |
Petrolina: Embrapa Semi-Árido, 1999. |
Páginas: |
Np. |
Descrição Física: |
il. |
Série: |
(Embrapa Semi-Árido. Instruções técnicas, 21). |
Idioma: |
Português |
Conteúdo: |
A Osmose Inversa tornou-se um processo bem estabelecido para produção de água potável a partir da água salina ou do mar. O que são incrustações; Scaling; Prevenção do Scaling; Conclusão. |
Palavras-Chave: |
Brasil; Desalting; Dessalinizacção; Dessalizacao; Incrustação; Incrustacoes; Inverse osmosis; Osmose inversa; Pernambuco; Petrolina; Prevenção; Prevention; Processing; Scaling; Water desalting. |
Thesagro: |
Água; Água Potável; Água Salgada; Água Salina; Água Salobra; Dessalinização; Osmose; Processamento. |
Thesaurus NAL: |
Desalination; drinking water; fouling; osmosis; reverse osmosis; saline water; Water. |
Categoria do assunto: |
-- X Pesquisa, Tecnologia e Engenharia |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/256239/1/Caracterizacao-e-prevencao-Instrucoes-Tecnicas-21.pdf
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Marc: |
LEADER 01549nam a2200517 a 4500 001 1131456 005 2023-09-29 008 1999 bl uuuu u0uu1 u #d 100 1 $aAMORIM, M. C. C. de 245 $aCaracterização e prevenção de incrustações em sistemas de dessalinização por osmose inversa. 260 $aPetrolina: Embrapa Semi-Árido$c1999 300 $aNp.$cil. 490 $a(Embrapa Semi-Árido. Instruções técnicas, 21). 520 $aA Osmose Inversa tornou-se um processo bem estabelecido para produção de água potável a partir da água salina ou do mar. O que são incrustações; Scaling; Prevenção do Scaling; Conclusão. 650 $aDesalination 650 $adrinking water 650 $afouling 650 $aosmosis 650 $areverse osmosis 650 $asaline water 650 $aWater 650 $aÁgua 650 $aÁgua Potável 650 $aÁgua Salgada 650 $aÁgua Salina 650 $aÁgua Salobra 650 $aDessalinização 650 $aOsmose 650 $aProcessamento 653 $aBrasil 653 $aDesalting 653 $aDessalinizacção 653 $aDessalizacao 653 $aIncrustação 653 $aIncrustacoes 653 $aInverse osmosis 653 $aOsmose inversa 653 $aPernambuco 653 $aPetrolina 653 $aPrevenção 653 $aPrevention 653 $aProcessing 653 $aScaling 653 $aWater desalting 700 1 $aSILVA JUNIOR, L. G. de A. 700 1 $aPORTO, E. R.
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