Edīte Kaufmane, Kaspars Sudars, Ivars Namatēvs, Ieva Kalniņa, Jānis Judvaitis, Rihards Balašs, Sarmīte Strautiņa. QuinceSet: Dataset of annotated Japanese quince images for object detection. Data in Brief, 42(), 108332 pp. Elsevier, 2022.

Bibtex citāts:
author = {Edīte Kaufmane and Kaspars Sudars and Ivars Namatēvs and Ieva Kalniņa and Jānis Judvaitis and Rihards Balašs and Sarmīte Strautiņa},
title = {QuinceSet: Dataset of annotated Japanese quince images for object detection},
journal = {Data in Brief},
volume = {42},
pages = {108332},
publisher = {Elsevier},
year = {2022}

Anotācija: With long-term changes in temperature and weather patterns, ecologically adaptable fruit varieties are becoming increasingly important in agriculture. For selection of candidate cultivars in fruit breeding or for yield predictions, fruit set characteristics at different growth stages need to be described and evaluated, which is largely done visually. This is a time-consuming and labor-intensive process that also requires sufficient expert knowledge. The annotated dataset for Japanese quince - QuinceSet - consists of images of Japanese quince (Chaenomeles japonica) fruits taken at two phenological developmental stages and annotated for detection and phenotyping. First, after flowering, when the second fruit fall is over and the fruits have reached 30-50% of their final size, and second, at the ripening stage of quince, just before the fruits are yielded. Both stages of quince images classified as unripe and ripe were annotated using ground truth ROI and presented in YOLO format. The dataset contains 1515 high-resolution RGB .jpg images with the same number of annotated .txt files. Images in the dataset were manually annotated using LabelImg software. A total of 17,171 annotations were provided by the experts. The images were acquired on site at the Institute of Horticulture in Dobele, Latvia. Homogenization of the images was performed under different weather conditions, at different times of the day, and from different capturing angles. The dataset contains both fully visible quinces and quinces partially obscured by leaves. Care was also taken to ensure that the foreground, which contains the leaves has adequate brightness with minimal shadows, while the background is darker. The presented dataset will allow to increase the efficiency of the breeding process and yield estimation, to identify and phenotype quinces more reliably, and may also be useful for breeding other crops.

URL: https://doi.org/10.1016/j.dib.2022.108332

Žurnāla kvartile: Q2

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