Use of RGB images from unmanned aerial vehicle to estimate lettuce growth in root-knot nematode infested soil
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.atech.2022.100100 http://hdl.handle.net/11449/246847 |
Resumo: | Lettuce (Lactuca sativa) is an important horticultural commodity all over the world, and its growth can be affected by root-knot nematodes (Meloidogyne spp.). To keep track of plant behaviors, growers are using new technologies. In this paper, aerial images were obtained using a low-cost unmanned aerial vehicle (UAV) to gather crop information in a short time giving acceptable accuracy for decision-making in the field. Evaluations were done to check the flight height interference in the image's quality for lettuce mapping, and select the best one to estimate the effect of root-knot nematode incidence on lettuce growth. In a field infested with M. incognita, lettuce seedlings were planted in plots treated with bionematicide and control plots. Aerial images were obtained using low-cost UAV in four flight heights performed for five weeks, along with field measurements. Images were processed and used to calculate vegetation indices (VI) and vegetation cover (VC). After lettuce harvesting, nematode eggs were extracted from plants' roots and quantified. Plots treated with bionematicide showed no difference from the control plots in eggs number and lettuce growth. Differences in VI values between the flight heights were not consistent, suggesting that VI values could be affected by the lack of luminosity calibration in each flight condition. VC values calculated from field data presented strong positive correlations with VI and VC values from UAV image data, indicating that RGB images obtained by UAV can be used in the detection of diseases that affect plant growth, as well as following up harvesting time. |
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Use of RGB images from unmanned aerial vehicle to estimate lettuce growth in root-knot nematode infested soilBacillus subtilisBiological controlLactuca sativaMeloidogyne incognitaVegetation coverVegetation indexLettuce (Lactuca sativa) is an important horticultural commodity all over the world, and its growth can be affected by root-knot nematodes (Meloidogyne spp.). To keep track of plant behaviors, growers are using new technologies. In this paper, aerial images were obtained using a low-cost unmanned aerial vehicle (UAV) to gather crop information in a short time giving acceptable accuracy for decision-making in the field. Evaluations were done to check the flight height interference in the image's quality for lettuce mapping, and select the best one to estimate the effect of root-knot nematode incidence on lettuce growth. In a field infested with M. incognita, lettuce seedlings were planted in plots treated with bionematicide and control plots. Aerial images were obtained using low-cost UAV in four flight heights performed for five weeks, along with field measurements. Images were processed and used to calculate vegetation indices (VI) and vegetation cover (VC). After lettuce harvesting, nematode eggs were extracted from plants' roots and quantified. Plots treated with bionematicide showed no difference from the control plots in eggs number and lettuce growth. Differences in VI values between the flight heights were not consistent, suggesting that VI values could be affected by the lack of luminosity calibration in each flight condition. VC values calculated from field data presented strong positive correlations with VI and VC values from UAV image data, indicating that RGB images obtained by UAV can be used in the detection of diseases that affect plant growth, as well as following up harvesting time.Department of Agriculture Federal University of Lavras (UFLA), P.O. Box: 3037, 37.200-900, MGDepartment of Phytopathology Federal University of Lavras (UFLA), P.O. Box: 3037, 37.200-900, MGDepartment of Plant Production University of São Paulo State (UNESP), SPDepartment of Plant Production University of São Paulo State (UNESP), SPUniversidade Federal de Lavras (UFLA)Universidade Estadual Paulista (UNESP)Cavalcanti, Vytória Piscitellidos Santos, Adão FelipeRodrigues, Filipe AlmendagnaTerra, Willian CésarAraújo, Ronilson CarlosRibeiro, Clerio RodriguesCampos, Vicente PauloRigobelo, Everlon Cid [UNESP]Medeiros, Flávio Henrique VasconcelosDória, Joyce2023-07-29T12:52:06Z2023-07-29T12:52:06Z2023-02-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.atech.2022.100100Smart Agricultural Technology, v. 3.2772-3755http://hdl.handle.net/11449/24684710.1016/j.atech.2022.1001002-s2.0-85148349094Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengSmart Agricultural Technologyinfo:eu-repo/semantics/openAccess2023-07-29T12:52:06Zoai:repositorio.unesp.br:11449/246847Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T18:51:21.291314Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Use of RGB images from unmanned aerial vehicle to estimate lettuce growth in root-knot nematode infested soil |
title |
Use of RGB images from unmanned aerial vehicle to estimate lettuce growth in root-knot nematode infested soil |
spellingShingle |
Use of RGB images from unmanned aerial vehicle to estimate lettuce growth in root-knot nematode infested soil Cavalcanti, Vytória Piscitelli Bacillus subtilis Biological control Lactuca sativa Meloidogyne incognita Vegetation cover Vegetation index |
title_short |
Use of RGB images from unmanned aerial vehicle to estimate lettuce growth in root-knot nematode infested soil |
title_full |
Use of RGB images from unmanned aerial vehicle to estimate lettuce growth in root-knot nematode infested soil |
title_fullStr |
Use of RGB images from unmanned aerial vehicle to estimate lettuce growth in root-knot nematode infested soil |
title_full_unstemmed |
Use of RGB images from unmanned aerial vehicle to estimate lettuce growth in root-knot nematode infested soil |
title_sort |
Use of RGB images from unmanned aerial vehicle to estimate lettuce growth in root-knot nematode infested soil |
author |
Cavalcanti, Vytória Piscitelli |
author_facet |
Cavalcanti, Vytória Piscitelli dos Santos, Adão Felipe Rodrigues, Filipe Almendagna Terra, Willian César Araújo, Ronilson Carlos Ribeiro, Clerio Rodrigues Campos, Vicente Paulo Rigobelo, Everlon Cid [UNESP] Medeiros, Flávio Henrique Vasconcelos Dória, Joyce |
author_role |
author |
author2 |
dos Santos, Adão Felipe Rodrigues, Filipe Almendagna Terra, Willian César Araújo, Ronilson Carlos Ribeiro, Clerio Rodrigues Campos, Vicente Paulo Rigobelo, Everlon Cid [UNESP] Medeiros, Flávio Henrique Vasconcelos Dória, Joyce |
author2_role |
author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de Lavras (UFLA) Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Cavalcanti, Vytória Piscitelli dos Santos, Adão Felipe Rodrigues, Filipe Almendagna Terra, Willian César Araújo, Ronilson Carlos Ribeiro, Clerio Rodrigues Campos, Vicente Paulo Rigobelo, Everlon Cid [UNESP] Medeiros, Flávio Henrique Vasconcelos Dória, Joyce |
dc.subject.por.fl_str_mv |
Bacillus subtilis Biological control Lactuca sativa Meloidogyne incognita Vegetation cover Vegetation index |
topic |
Bacillus subtilis Biological control Lactuca sativa Meloidogyne incognita Vegetation cover Vegetation index |
description |
Lettuce (Lactuca sativa) is an important horticultural commodity all over the world, and its growth can be affected by root-knot nematodes (Meloidogyne spp.). To keep track of plant behaviors, growers are using new technologies. In this paper, aerial images were obtained using a low-cost unmanned aerial vehicle (UAV) to gather crop information in a short time giving acceptable accuracy for decision-making in the field. Evaluations were done to check the flight height interference in the image's quality for lettuce mapping, and select the best one to estimate the effect of root-knot nematode incidence on lettuce growth. In a field infested with M. incognita, lettuce seedlings were planted in plots treated with bionematicide and control plots. Aerial images were obtained using low-cost UAV in four flight heights performed for five weeks, along with field measurements. Images were processed and used to calculate vegetation indices (VI) and vegetation cover (VC). After lettuce harvesting, nematode eggs were extracted from plants' roots and quantified. Plots treated with bionematicide showed no difference from the control plots in eggs number and lettuce growth. Differences in VI values between the flight heights were not consistent, suggesting that VI values could be affected by the lack of luminosity calibration in each flight condition. VC values calculated from field data presented strong positive correlations with VI and VC values from UAV image data, indicating that RGB images obtained by UAV can be used in the detection of diseases that affect plant growth, as well as following up harvesting time. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07-29T12:52:06Z 2023-07-29T12:52:06Z 2023-02-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/j.atech.2022.100100 Smart Agricultural Technology, v. 3. 2772-3755 http://hdl.handle.net/11449/246847 10.1016/j.atech.2022.100100 2-s2.0-85148349094 |
url |
http://dx.doi.org/10.1016/j.atech.2022.100100 http://hdl.handle.net/11449/246847 |
identifier_str_mv |
Smart Agricultural Technology, v. 3. 2772-3755 10.1016/j.atech.2022.100100 2-s2.0-85148349094 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Smart Agricultural Technology |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128992281100288 |