Development of a robotic structure for acquisition and classification of images (ERACI) in sugarcane crops
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista ciência agronômica (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902020000500413 |
Resumo: | ABSTRACT Digital agriculture contributes to agricultural efficiency through the use of such tools as computer vision, robotics, and precision agriculture. In this study, the objective was to develop a system capable of classifying images through the recognition of pre-established patterns. For this purpose, a geographically distributed system was created, based on the Raspberry Pi 3B+ computer, which captures images in the field and stores them in a database, where they are available to receive a pre-classification by a supervisor. Subsequently, classifiers are generated, evaluated, and sent to the remote device to conduct a classification in real time. For an evaluation of the system, 23 classes were defined and grouped into 3 superclasses, 36,979 images were captured, and 1,579 pre-classifications were conducted, which allowed the classification tests to be carried out by means of a cross-validation by randomly dividing into the equivalent number of classes. These tests revealed that the accuracy delivered by each classifier is different and directly proportional to the quantity and balance of the samples, with a variation of 11% to 79%, with 26 and 2,200 samples considered, respectively. The response time of the system was evaluated during 1,585 periods and was maintained within approximately 0.20 s, and under controlled speed of the vehicle, can be used for the dispersion of inputs in real time. |
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Development of a robotic structure for acquisition and classification of images (ERACI) in sugarcane cropsDigital AgricultureMachine LearningOpen sourceRaspberry PiComputer VisionABSTRACT Digital agriculture contributes to agricultural efficiency through the use of such tools as computer vision, robotics, and precision agriculture. In this study, the objective was to develop a system capable of classifying images through the recognition of pre-established patterns. For this purpose, a geographically distributed system was created, based on the Raspberry Pi 3B+ computer, which captures images in the field and stores them in a database, where they are available to receive a pre-classification by a supervisor. Subsequently, classifiers are generated, evaluated, and sent to the remote device to conduct a classification in real time. For an evaluation of the system, 23 classes were defined and grouped into 3 superclasses, 36,979 images were captured, and 1,579 pre-classifications were conducted, which allowed the classification tests to be carried out by means of a cross-validation by randomly dividing into the equivalent number of classes. These tests revealed that the accuracy delivered by each classifier is different and directly proportional to the quantity and balance of the samples, with a variation of 11% to 79%, with 26 and 2,200 samples considered, respectively. The response time of the system was evaluated during 1,585 periods and was maintained within approximately 0.20 s, and under controlled speed of the vehicle, can be used for the dispersion of inputs in real time.Universidade Federal do Ceará2020-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902020000500413Revista Ciência Agronômica v.51 n.spe 2020reponame:Revista ciência agronômica (Online)instname:Universidade Federal do Ceará (UFC)instacron:UFC10.5935/1806-6690.20200102info:eu-repo/semantics/openAccessCardoso,José Ricardo FerreiraFurlani,Carlos Eduardo AngeliTurco,José Eduardo PitelliZerbato,CristianoCarneiro,Franciele MorlinEstevam,Francisca Nivanda de Limaeng2021-08-17T00:00:00Zoai:scielo:S1806-66902020000500413Revistahttp://www.ccarevista.ufc.br/PUBhttps://old.scielo.br/oai/scielo-oai.php||alekdutra@ufc.br|| ccarev@ufc.br1806-66900045-6888opendoar:2021-08-17T00:00Revista ciência agronômica (Online) - Universidade Federal do Ceará (UFC)false |
dc.title.none.fl_str_mv |
Development of a robotic structure for acquisition and classification of images (ERACI) in sugarcane crops |
title |
Development of a robotic structure for acquisition and classification of images (ERACI) in sugarcane crops |
spellingShingle |
Development of a robotic structure for acquisition and classification of images (ERACI) in sugarcane crops Cardoso,José Ricardo Ferreira Digital Agriculture Machine Learning Open source Raspberry Pi Computer Vision |
title_short |
Development of a robotic structure for acquisition and classification of images (ERACI) in sugarcane crops |
title_full |
Development of a robotic structure for acquisition and classification of images (ERACI) in sugarcane crops |
title_fullStr |
Development of a robotic structure for acquisition and classification of images (ERACI) in sugarcane crops |
title_full_unstemmed |
Development of a robotic structure for acquisition and classification of images (ERACI) in sugarcane crops |
title_sort |
Development of a robotic structure for acquisition and classification of images (ERACI) in sugarcane crops |
author |
Cardoso,José Ricardo Ferreira |
author_facet |
Cardoso,José Ricardo Ferreira Furlani,Carlos Eduardo Angeli Turco,José Eduardo Pitelli Zerbato,Cristiano Carneiro,Franciele Morlin Estevam,Francisca Nivanda de Lima |
author_role |
author |
author2 |
Furlani,Carlos Eduardo Angeli Turco,José Eduardo Pitelli Zerbato,Cristiano Carneiro,Franciele Morlin Estevam,Francisca Nivanda de Lima |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Cardoso,José Ricardo Ferreira Furlani,Carlos Eduardo Angeli Turco,José Eduardo Pitelli Zerbato,Cristiano Carneiro,Franciele Morlin Estevam,Francisca Nivanda de Lima |
dc.subject.por.fl_str_mv |
Digital Agriculture Machine Learning Open source Raspberry Pi Computer Vision |
topic |
Digital Agriculture Machine Learning Open source Raspberry Pi Computer Vision |
description |
ABSTRACT Digital agriculture contributes to agricultural efficiency through the use of such tools as computer vision, robotics, and precision agriculture. In this study, the objective was to develop a system capable of classifying images through the recognition of pre-established patterns. For this purpose, a geographically distributed system was created, based on the Raspberry Pi 3B+ computer, which captures images in the field and stores them in a database, where they are available to receive a pre-classification by a supervisor. Subsequently, classifiers are generated, evaluated, and sent to the remote device to conduct a classification in real time. For an evaluation of the system, 23 classes were defined and grouped into 3 superclasses, 36,979 images were captured, and 1,579 pre-classifications were conducted, which allowed the classification tests to be carried out by means of a cross-validation by randomly dividing into the equivalent number of classes. These tests revealed that the accuracy delivered by each classifier is different and directly proportional to the quantity and balance of the samples, with a variation of 11% to 79%, with 26 and 2,200 samples considered, respectively. The response time of the system was evaluated during 1,585 periods and was maintained within approximately 0.20 s, and under controlled speed of the vehicle, can be used for the dispersion of inputs in real time. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902020000500413 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902020000500413 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.5935/1806-6690.20200102 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Universidade Federal do Ceará |
publisher.none.fl_str_mv |
Universidade Federal do Ceará |
dc.source.none.fl_str_mv |
Revista Ciência Agronômica v.51 n.spe 2020 reponame:Revista ciência agronômica (Online) instname:Universidade Federal do Ceará (UFC) instacron:UFC |
instname_str |
Universidade Federal do Ceará (UFC) |
instacron_str |
UFC |
institution |
UFC |
reponame_str |
Revista ciência agronômica (Online) |
collection |
Revista ciência agronômica (Online) |
repository.name.fl_str_mv |
Revista ciência agronômica (Online) - Universidade Federal do Ceará (UFC) |
repository.mail.fl_str_mv |
||alekdutra@ufc.br|| ccarev@ufc.br |
_version_ |
1750297490207277056 |