|
|
Registros recuperados : 31 | |
10. | | YOU, C. B.; SONG, W.; WANG, H. X.; LI, J. P.; LIN, M.; HAI, W. L. Association of Alcaligenes faecalis with wetland rice. Plant Soil, v.137, n.1, p.81-85, 1991. Biblioteca(s): Embrapa Agrobiologia. |
| |
12. | | REDMER, D. A.; DAI, Y.; LI, J.; CHARNOCK-JONES, D.S.; SMITH, S. K.; REYNOLDS, L. P.; MOOR, R. M. Characterization and expression of vascular endothelial growth factor (VEGF) in the ovine corpus luteum. Journal of Reproduction and Fertility, v.108, n.1, p.157-165, 1996. Biblioteca(s): Embrapa Caprinos e Ovinos. |
| |
13. | | AN, F.; FAN, J.; LI, J.; LI, Q. X.; LI, K.; ZHU, W.; WEN, F.; CARVALHO, L. J. C. B.; CHEN, S. Comparison of leaf proteomes of cassava (Manihot esculenta crantz) cultivar NZ199 diploid and autotetraploid genotypes. PLoS ONE, v. 9, n. 4, 2014. (Open access) Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
| |
14. | | ZHU, H.; ZHANG, H.; WANG, Y.; CIREN, D.; DONG, H.; WU, Q.; REHMAN, M. U.; NABI, F.; MEHMOOD, K.; LI, J. Phylogenetic and pathotypic characterization of newcastle disease virus in Tibetan chickens, China. Pesquisa Veterinária Brasileira, Rio de Janeiro, v. 38, n. 1, p. 37-40, janeiro 2018. Biblioteca(s): Embrapa Unidades Centrais. |
| |
15. | | AN, F.; CHEN, T.; STÉPHANIE, D. M. A.; LI, K.; LI, Q. X.; CARVALHO, L. J. C. B.; TOMLINS, K.; LI, J.; GU, B.; CHEN, S. Domestication syndrome is investigated by proteomic analysis between cultivated cassava (Manihot esculenta Crantz) and Its wild relatives. PLoS ONE, v. 11, n. 3, 2016. Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
| |
16. | | OSCO, L. P.; ARRUDA, M. S.; GONÇALVES, D. N.; DIAS, A.; BATISTOTI, J.; SOUZA, M.; GOMES, F. D. G.; RAMOS, A. P. M.; JORGE, L. A. de C.; LIESENBERG, V.; LI, J.; MA, L.; MARCATO JUNIOR, J.; GONÇALVES, W. N. A CNN approach to simultaneously count plants and detect plantation-rows from UAV imagery. ISPRS Journal of Photogrammetry and Remote Sensing, v. 174, 2021. 1 - 17 Biblioteca(s): Embrapa Instrumentação. |
| |
17. | | LU, Y.; YAN, J.; GUIMARAES, C. T.; TABA, S.; HAO, Z.; GAO, S.; CHEN, S.; LI, J.; ZHANG, S.; VIVEK, B. S.; MAGOROKOSHO, C.; MUGO, S.; MAKUMBI, D.; PARENTONI, S. N.; SHAH, T.; RONG, T.; CROUCH, J. H.; XU, Y. Molecular characterization of global maize breeding germplasm based on genome-wide single nucleotide polymorphisms Theoretical and Applied Genetics, New York, v. 120, n. 1 p. 93-115, Dec. 2009. Biblioteca(s): Embrapa Milho e Sorgo. |
| |
18. | | OSCO, L. P.; MARCATO JUNIOR, J.; RAMOS, A. P. M.; JORGE, L. A. de C.; FATHOLAHI, S. N.; SILVA, J. A.; MATSUBARA, E. T.; PISTORI, H.; GONÇALVES, W. N.; LI, J. A review on deep learning in UAV remote sensing. International Journal of Applied Earth Observations and Geoinformation, v. 102, 102456, 2021. 1 - 22 Biblioteca(s): Embrapa Instrumentação. |
| |
19. | | SABINO, A. R.; TAVARES, S. S.; RIFFEL, A.; LI, J. V.; OLIVEIRA, D. J. A.; FERES, C. I. M. A.; HENRIQUE, L.; OLIVEIRA, J. S.; CORREIA, G. D. S.; SABINO, A. R.; NASCIMENTO, T. G.; HAWKES, G.; SANTANA, A. E. G.; HOLMES, E.; BENDO, E. S. 1H NMR metabolomic approach reveals chlorogenic acid as a response ofsugarcane induced by exposure toDiatraea saccharalis. Industrial Crops & Products, V. 140, 111651, 2019. Biblioteca(s): Embrapa Tabuleiros Costeiros. |
| |
20. | | OSCO, L. P.; RAMOS, A. P. M.; PINHEIRO, M. M. F.; MORIYA, E. A. S.; IMAI, N. N.; ESTRABIS, N.; IANCZYK, F.; ARAÚJO, F. F.; LIESENBERG, V.; JORGE, L. A. de C.; LI, J.; MA, L.; GONÇALVES, W. N.; MARCATO JUNIOR, J.; CRESTE, J. E. A machine learning framework to predict nutrient content in valencia-orange leaf hyperspectral measurements. Remote Sensing, n. 12, v. 6, a. 906, 2020. 1 - 21 Biblioteca(s): Embrapa Instrumentação. |
| |
Registros recuperados : 31 | |
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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 |
Circulação/Nível: |
A - 1 |
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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