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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
28/08/2023 |
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Data da última atualização: |
26/10/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
CASTRO, V. H. M. e de; PARREIRAS, T. C.; BOLFE, E. L. |
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Afiliação: |
VICTÓRIA HELLENA MATUSEVICIUS E DE CASTRO, UNIVERSIDADE ESTADUAL DE CAMPINAS; TAYA CRISTO PARREIRAS, UNIVERSIDADE ESTADUAL DE CAMPINAS; EDSON LUIS BOLFE, CNPTIA, UNIVERSIDADE ESTADUAL DE CAMPINAS. |
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Título: |
Disease detection in citrus crops using optical and thermal remote sensing: a literature review. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Engenharia na Agricultura, v. 31, p. 140-157, 2023. |
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ISSN: |
2175-6813 |
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DOI: |
https://doi.org/10.13083/reveng.v30i1.15448 |
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Idioma: |
Português |
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Notas: |
Errata - The acknowledgments of the article include: To the State of São Paulo Research Foundation (FAPESP), process number 2019/26222-6. The correct process number is 2022/09319-9. |
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Conteúdo: |
Brazil stands out in the international citrus trade, especially due to its oranges, having produced around 16 million tons in 2021. However, productivity could be increased with greater control of diseases such as greening, which has spread around the world and leads to the total loss of affected trees. Given this scenario, it is necessary to perform fast and accurate detections in order to better manage actions and inputs. Since remote sensing is a pillar of digital agriculture, a literature review was carried out to analyze the use of optical and thermal sensors for the detection of diseases that affect citrus groves. For this purpose, the international databases Scopus and Web of Science were used to select references published between 2012 and 2022, resulting in twelve studies - most from China or the United States of America. The results showed a prevalence of methodologies that combine bands and spectral indices obtained through the use of multispectral and hyperspectral sensors, predominantly on board unmanned aircrafts (UAVs). Machine learning (ML) and deep learning (DL) classification algorithms produced good results in the detection of citrus groves affected by diseases, mainly greening. These results are affected by the stage of the infection, the presence or absence of symptoms, and the spectral and spatial resolutions of the sensors: the Red-Edge band and data with higher spatial detail result in more accurate classification models. However, the analyzed literature is still inconclusive regarding the early detection of infected plants. MenosBrazil stands out in the international citrus trade, especially due to its oranges, having produced around 16 million tons in 2021. However, productivity could be increased with greater control of diseases such as greening, which has spread around the world and leads to the total loss of affected trees. Given this scenario, it is necessary to perform fast and accurate detections in order to better manage actions and inputs. Since remote sensing is a pillar of digital agriculture, a literature review was carried out to analyze the use of optical and thermal sensors for the detection of diseases that affect citrus groves. For this purpose, the international databases Scopus and Web of Science were used to select references published between 2012 and 2022, resulting in twelve studies - most from China or the United States of America. The results showed a prevalence of methodologies that combine bands and spectral indices obtained through the use of multispectral and hyperspectral sensors, predominantly on board unmanned aircrafts (UAVs). Machine learning (ML) and deep learning (DL) classification algorithms produced good results in the detection of citrus groves affected by diseases, mainly greening. These results are affected by the stage of the infection, the presence or absence of symptoms, and the spectral and spatial resolutions of the sensors: the Red-Edge band and data with higher spatial detail result in more accurate classification models. However, the analyzed literat... Mostrar Tudo |
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Palavras-Chave: |
Agricultura digital; Algoritmos de aprendizado de máquina; Citriculture; Digital agriculture; NDVI. |
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Thesagro: |
Citricultura; Doença de Planta; Sensoriamento Remoto. |
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Thesaurus Nal: |
Citrus; Greening disease; Plant diseases and disorders; Remote sensing. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/257157/1/AP-Disease-detection-2023.pdf
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Marc: |
LEADER 02737naa a2200325 a 4500
001 2156159
005 2023-10-26
008 2023 bl uuuu u00u1 u #d
022 $a2175-6813
024 7 $ahttps://doi.org/10.13083/reveng.v30i1.15448$2DOI
100 1 $aCASTRO, V. H. M. e de
245 $aDisease detection in citrus crops using optical and thermal remote sensing$ba literature review.$h[electronic resource]
260 $c2023
500 $aErrata - The acknowledgments of the article include: To the State of São Paulo Research Foundation (FAPESP), process number 2019/26222-6. The correct process number is 2022/09319-9.
520 $aBrazil stands out in the international citrus trade, especially due to its oranges, having produced around 16 million tons in 2021. However, productivity could be increased with greater control of diseases such as greening, which has spread around the world and leads to the total loss of affected trees. Given this scenario, it is necessary to perform fast and accurate detections in order to better manage actions and inputs. Since remote sensing is a pillar of digital agriculture, a literature review was carried out to analyze the use of optical and thermal sensors for the detection of diseases that affect citrus groves. For this purpose, the international databases Scopus and Web of Science were used to select references published between 2012 and 2022, resulting in twelve studies - most from China or the United States of America. The results showed a prevalence of methodologies that combine bands and spectral indices obtained through the use of multispectral and hyperspectral sensors, predominantly on board unmanned aircrafts (UAVs). Machine learning (ML) and deep learning (DL) classification algorithms produced good results in the detection of citrus groves affected by diseases, mainly greening. These results are affected by the stage of the infection, the presence or absence of symptoms, and the spectral and spatial resolutions of the sensors: the Red-Edge band and data with higher spatial detail result in more accurate classification models. However, the analyzed literature is still inconclusive regarding the early detection of infected plants.
650 $aCitrus
650 $aGreening disease
650 $aPlant diseases and disorders
650 $aRemote sensing
650 $aCitricultura
650 $aDoença de Planta
650 $aSensoriamento Remoto
653 $aAgricultura digital
653 $aAlgoritmos de aprendizado de máquina
653 $aCitriculture
653 $aDigital agriculture
653 $aNDVI
700 1 $aPARREIRAS, T. C.
700 1 $aBOLFE, E. L.
773 $tEngenharia na Agricultura$gv. 31, p. 140-157, 2023.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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Registro Completo
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Biblioteca(s): |
Embrapa Hortaliças. |
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Data corrente: |
10/02/2014 |
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Data da última atualização: |
10/02/2014 |
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Tipo da produção científica: |
Artigo de Divulgação na Mídia |
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Autoria: |
MOURA, A. P. de; GUIMARAES, J. A.; MICHEREFF FILHO, M. |
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Afiliação: |
ALEXANDRE PINHO DE MOURA, CNPH; JORGE ANDERSON GUIMARAES, CNPH; MIGUEL MICHEREFF FILHO, CNPH. |
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Título: |
Trichogramma pretiosum: o pequeno grande aliado. |
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Ano de publicação: |
2013 |
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Fonte/Imprenta: |
Revista Campo & Negócios HF, Uberlândia, Ano 8, n. 92, p. 41-43, 2013. |
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Idioma: |
Português |
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Thesagro: |
Controle biológico. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/96906/1/MOURA-920001.pdf
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Marc: |
LEADER 00435nam a2200133 a 4500
001 1979366
005 2014-02-10
008 2013 bl uuuu u00u1 u #d
100 1 $aMOURA, A. P. de
245 $aTrichogramma pretiosum$bo pequeno grande aliado.$h[electronic resource]
260 $aRevista Campo & Negócios HF, Uberlândia, Ano 8, n. 92, p. 41-43$c2013
650 $aControle biológico
700 1 $aGUIMARAES, J. A.
700 1 $aMICHEREFF FILHO, M.
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Embrapa Hortaliças (CNPH) |
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