Please use this identifier to cite or link to this item: 10.3390/data8050086
Title: RaspberrySet : Dataset of Annotated Raspberry Images for Object Detection
Authors: Strautiņa, Sarmīte
Kalniņa, Ieva
Kaufmane, Edīte
Sudars, Kaspars
Namatēvs, Ivars
Nikulins, Arturs
Edelmers, Edgars
Keywords: berry detection;computer vision;precision horticulture;rubus idaeus;1.2 Computer and information sciences;1.1. Scientific article indexed in Web of Science and/or Scopus database;Information Systems;Computer Science Applications;Information Systems and Management
Issue Date: May-2023
Citation: Strautiņa , S , Kalniņa , I , Kaufmane , E , Sudars , K , Namatēvs , I , Nikulins , A & Edelmers , E 2023 , ' RaspberrySet : Dataset of Annotated Raspberry Images for Object Detection ' , Data , vol. 8 , no. 5 , 86 . https://doi.org/10.3390/data8050086
Abstract: 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 because long-term changes in temperature and weather patterns have made it increasingly important for crops to be able to adapt to their environment. To assess the suitability of different cultivars or to make yield predictions, it is necessary to describe and evaluate berries’ characteristics at various growth stages. 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 this process, and it includes images of raspberry berries at five different stages of development. These stages are flower buds, flowers, unripe berries, and ripe berries. All these stages of raspberry images classified buds, damaged buds, flowers, unripe berries, and ripe berries and were annotated using ground truth ROI and presented in YOLO format. The dataset includes 2039 high-resolution RGB images, with a total of 46,659 annotations provided by experts using Label Studio software (1.7.1). The images were taken in various weather conditions, at different times of the day, and from different angles, and they include fully visible buds, flowers, berries, and partially obscured buds. This dataset is intended to improve the efficiency of berry breeding and yield estimation and to identify the raspberry phenotype more accurately. It may also be useful for breeding other fruit crops, as it allows for the reliable detection and phenotyping of yield components at different stages of development. By providing 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. Dataset: https://doi.org/10.5281/zenodo.7014728 Dataset License: CC BY 4.0.
Description: Funding Information: This research and APC were funded by the Latvian Council of Science, grant number lzp-2020/1-0353 “Smart noninvasive phenotyping of raspberries and Japanese quinces using machine learning and hyperspectral and 3D imaging”. Publisher Copyright: © 2023 by the authors.
DOI: 10.3390/data8050086
ISSN: 2306-5729
Appears in Collections:Research outputs from Pure / Zinātniskās darbības rezultāti no ZDIS Pure

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