Bean leaf image dataset annotated with leaf dimensions, segmentation masks, and camera calibrationMendeley Data
Leaf dimensioning is relevant for analyzing plant responses to several conditions such as soil fertility, availability of light, agricultural pesticide effect, and access to water in the soil or periods of drought. In this paper, we present a dataset composed of 6981 images of 612 common bean leaves...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925000605 |
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| author | Karla Gabriele Florentino da Silva Paulo Victor de Magalhães Rozatto Kaio de Oliveira e Sousa Lucas Dias Hudson Artur Welerson Sott Meyer Alemilson Fabiano Silva Igor Tibiriçá Mendes Alex Rodrigues Borges Leandro Elias Morais Luiz Maurílio da Silva Maciel Saulo Moraes Villela Helio Pedrini Marcelo Bernardes Vieira |
| author_facet | Karla Gabriele Florentino da Silva Paulo Victor de Magalhães Rozatto Kaio de Oliveira e Sousa Lucas Dias Hudson Artur Welerson Sott Meyer Alemilson Fabiano Silva Igor Tibiriçá Mendes Alex Rodrigues Borges Leandro Elias Morais Luiz Maurílio da Silva Maciel Saulo Moraes Villela Helio Pedrini Marcelo Bernardes Vieira |
| author_sort | Karla Gabriele Florentino da Silva |
| collection | DOAJ |
| description | Leaf dimensioning is relevant for analyzing plant responses to several conditions such as soil fertility, availability of light, agricultural pesticide effect, and access to water in the soil or periods of drought. In this paper, we present a dataset composed of 6981 images of 612 common bean leaves (Phaseolus vulgaris). We captured the images of each leaf accompanied by a fiducial marker and annotated the known leaf dimensions (area, perimeter, length, and width). We provide annotations concerning image segmentation, known area uniformly distributed over the leaf region, real area of the marker region, marker pose, capture conditions, and camera calibration. This dataset can be useful for developing deep learning algorithms for leaf dimensioning and related problems. Therefore, there is a potential to contribute to computer vision and plant physiology researchers and specialists. |
| format | Article |
| id | doaj-art-9098d92eff8749c5b3d5bae9d75f3ce8 |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-9098d92eff8749c5b3d5bae9d75f3ce82025-01-31T05:11:51ZengElsevierData in Brief2352-34092025-04-0159111328Bean leaf image dataset annotated with leaf dimensions, segmentation masks, and camera calibrationMendeley DataKarla Gabriele Florentino da Silva0Paulo Victor de Magalhães Rozatto1Kaio de Oliveira e Sousa2Lucas Dias Hudson3Artur Welerson Sott Meyer4Alemilson Fabiano Silva5Igor Tibiriçá Mendes6Alex Rodrigues Borges7Leandro Elias Morais8Luiz Maurílio da Silva Maciel9Saulo Moraes Villela10Helio Pedrini11Marcelo Bernardes Vieira12Department of Computer Science, Federal University of Juiz de Fora, Juiz de Fora, MG, 36036-900, Brazil; Institute of Computing, University of Campinas, Campinas, SP, 13083-852, BrazilDepartment of Computer Science, Federal University of Juiz de Fora, Juiz de Fora, MG, 36036-900, BrazilDepartment of Computer Science, Federal University of Juiz de Fora, Juiz de Fora, MG, 36036-900, BrazilDepartment of Computer Science, Federal University of Juiz de Fora, Juiz de Fora, MG, 36036-900, BrazilDepartment of Computer Science, Federal University of Juiz de Fora, Juiz de Fora, MG, 36036-900, BrazilDepartment of Computer Science, Federal University of Juiz de Fora, Juiz de Fora, MG, 36036-900, BrazilDepartment of Computer Science, Federal University of Juiz de Fora, Juiz de Fora, MG, 36036-900, BrazilDepartment of Natural Sciences, Federal Institute of Education, Science and Technology of Minas Gerais, Ouro Branco, MG, 36494-018, BrazilDepartment of Natural Sciences, Federal Institute of Education, Science and Technology of Minas Gerais, Ouro Branco, MG, 36494-018, BrazilDepartment of Computer Science, Federal University of Juiz de Fora, Juiz de Fora, MG, 36036-900, Brazil; Corresponding author.Department of Computer Science, Federal University of Juiz de Fora, Juiz de Fora, MG, 36036-900, BrazilInstitute of Computing, University of Campinas, Campinas, SP, 13083-852, BrazilDepartment of Computer Science, Federal University of Juiz de Fora, Juiz de Fora, MG, 36036-900, BrazilLeaf dimensioning is relevant for analyzing plant responses to several conditions such as soil fertility, availability of light, agricultural pesticide effect, and access to water in the soil or periods of drought. In this paper, we present a dataset composed of 6981 images of 612 common bean leaves (Phaseolus vulgaris). We captured the images of each leaf accompanied by a fiducial marker and annotated the known leaf dimensions (area, perimeter, length, and width). We provide annotations concerning image segmentation, known area uniformly distributed over the leaf region, real area of the marker region, marker pose, capture conditions, and camera calibration. This dataset can be useful for developing deep learning algorithms for leaf dimensioning and related problems. Therefore, there is a potential to contribute to computer vision and plant physiology researchers and specialists.http://www.sciencedirect.com/science/article/pii/S2352340925000605Leaf measurementDeep learningSemantic segmentationFiducial markerArea estimation |
| spellingShingle | Karla Gabriele Florentino da Silva Paulo Victor de Magalhães Rozatto Kaio de Oliveira e Sousa Lucas Dias Hudson Artur Welerson Sott Meyer Alemilson Fabiano Silva Igor Tibiriçá Mendes Alex Rodrigues Borges Leandro Elias Morais Luiz Maurílio da Silva Maciel Saulo Moraes Villela Helio Pedrini Marcelo Bernardes Vieira Bean leaf image dataset annotated with leaf dimensions, segmentation masks, and camera calibrationMendeley Data Data in Brief Leaf measurement Deep learning Semantic segmentation Fiducial marker Area estimation |
| title | Bean leaf image dataset annotated with leaf dimensions, segmentation masks, and camera calibrationMendeley Data |
| title_full | Bean leaf image dataset annotated with leaf dimensions, segmentation masks, and camera calibrationMendeley Data |
| title_fullStr | Bean leaf image dataset annotated with leaf dimensions, segmentation masks, and camera calibrationMendeley Data |
| title_full_unstemmed | Bean leaf image dataset annotated with leaf dimensions, segmentation masks, and camera calibrationMendeley Data |
| title_short | Bean leaf image dataset annotated with leaf dimensions, segmentation masks, and camera calibrationMendeley Data |
| title_sort | bean leaf image dataset annotated with leaf dimensions segmentation masks and camera calibrationmendeley data |
| topic | Leaf measurement Deep learning Semantic segmentation Fiducial marker Area estimation |
| url | http://www.sciencedirect.com/science/article/pii/S2352340925000605 |
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