Bean leaf image dataset annotated with leaf dimensions, segmentation masks, and camera calibration

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 (<i>Phaseolus vulgaris</i>). 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.

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Publication Details

Journal
Data in Brief
Published
2025-01-27
DOI
https://doi.org/10.1016/j.dib.2025.111328
Citations
2
Primary Topic
Leaf Properties and Growth Measurement
Type
article
Field-Weighted Citation Impact
3.50

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article

Bean leaf image dataset annotated with leaf dimensions, segmentation masks, and camera calibration

Paulo Victor de Magalhães Rozatto, Karla Gabriele Florentino da Silva, Ana Maria Marques da Silva, Marcelo Bernardes Vieira et al.
2 citations
Data in Brief
Leaf Properties and Growth Measurement
3.50
article

Bean leaf image dataset annotated with leaf dimensions, segmentation masks, and camera calibration

Paulo Victor de Magalhães Rozatto, Karla Gabriele Florentino da Silva, Ana Maria Marques da Silva, Marcelo Bernardes Vieira, Luiz Maurílio da Silva Maciel, Leandro E. Morais, A. Meyer, Saulo Moraes Villela, Hélio Pedrini, Amadeu Borges, Igor Tibiriçá Mendes, Kaio de Oliveira E Sousa, Lucas Dias Hudson
article en
2 citations

Abstract

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 (<i>Phaseolus vulgaris</i>). 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.

Data in BriefVol. 59
Universidade Federal de Juiz de Fora (BR), Instituto Federal de Educação, Ciência e Tecnologia de Minas Gerais (BR), Universidade Estadual de Campinas (UNICAMP) (BR)
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Pesquisa do Estado de Minas Gerais
Openalex Percentile: Top 10%
Leaf Properties and Growth Measurement
3.50
DOIOpen Access PDFOpenAlex
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