分割
计算机科学
人工智能
正射影像
多光谱图像
自动化
RGB颜色模型
像素
模式识别(心理学)
计算机视觉
机械工程
工程类
作者
Jana Kierdorf,Laura Verena Junker‐Frohn,Mike Delaney,Mariele Donoso Olave,Andreas Burkart,H. Jaenicke,Onno Muller,Uwe Rascher,Ribana Roscher
出处
期刊:Cornell University - arXiv
日期:2022-01-01
被引量:1
标识
DOI:10.48550/arxiv.2204.00294
摘要
This article presents GrowliFlower, a georeferenced, image-based UAV time series dataset of two monitored cauliflower fields of size 0.39 and 0.60 ha acquired in 2020 and 2021. The dataset contains RGB and multispectral orthophotos from which about 14,000 individual plant coordinates are derived and provided. The coordinates enable the dataset users the extraction of complete and incomplete time series of image patches showing individual plants. The dataset contains collected phenotypic traits of 740 plants, including the developmental stage as well as plant and cauliflower size. As the harvestable product is completely covered by leaves, plant IDs and coordinates are provided to extract image pairs of plants pre and post defoliation, to facilitate estimations of cauliflower head size. Moreover, the dataset contains pixel-accurate leaf and plant instance segmentations, as well as stem annotations to address tasks like classification, detection, segmentation, instance segmentation, and similar computer vision tasks. The dataset aims to foster the development and evaluation of machine learning approaches. It specifically focuses on the analysis of growth and development of cauliflower and the derivation of phenotypic traits to foster the development of automation in agriculture. Two baseline results of instance segmentation at plant and leaf level based on the labeled instance segmentation data are presented. The entire data set is publicly available.
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