Bean leaf image dataset annotated with leaf dimensions, segmentation masks, and camera calibrationMendeley Data - 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; et al. (Data in Brief)
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.