Sarmīte Strautiņa, Ieva Kalniņa, Edīte Kaufmane, Kaspars Sudars, Ivars Namatēvs, Arturs Ņikuļins, Edgars Edelmers. RaspberrySet: Dataset of Annotated Raspberry Images for Object Detection. Data, 8(5), 5 pp. MDPI, 2023.

Bibtex citāts:
author = {Sarmīte Strautiņa and Ieva Kalniņa and Edīte Kaufmane and Kaspars Sudars and Ivars Namatēvs and Arturs Ņikuļins and Edgars Edelmers},
title = {RaspberrySet: Dataset of Annotated Raspberry Images for Object Detection},
journal = {Data},
volume = {8},
issue = {5},
pages = {5},
publisher = {MDPI},
year = {2023}

Anotācija: The RaspberrySet dataset is a valuable resource for those working in the field of agriculture,particularly in the selection and breeding of ecologically adaptable berry cultivars. This is becauselong-term changes in temperature and weather patterns have made it increasingly important for cropsto be able to adapt to their environment. To assess the suitability of different cultivars or to makeyield predictions, it is necessary to describe and evaluate berries’ characteristics at various growthstages. This process is typically carried out visually, but it can be time-consuming and labor-intensive,requiring significant expert knowledge. The RaspberrySet dataset was created to assist with thisprocess, and it includes images of raspberry berries at five different stages of development. Thesestages are flower buds, flowers, unripe berries, and ripe berries. All these stages of raspberry imagesclassified buds, damaged buds, flowers, unripe berries, and ripe berries and were annotated usingground truth ROI and presented in YOLO format. The dataset includes 2039 high-resolution RGBimages, with a total of 46,659 annotations provided by experts using Label Studio software (1.7.1). Theimages were taken in various weather conditions, at different times of the day, and from differentangles, and they include fully visible buds, flowers, berries, and partially obscured buds. This datasetis intended to improve the efficiency of berry breeding and yield estimation and to identify theraspberry phenotype more accurately. It may also be useful for breeding other fruit crops, as it allowsfor the reliable detection and phenotyping of yield components at different stages of development. Byproviding a homogenized dataset of images taken on-site at the Institute of Horticulture in Dobele,Latvia, the RaspberrySet dataset offers a valuable resource for those working in horticulture.


Žurnāla kvartile: Q2

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