Hydroponic farming plant life cycle dataset for AI driven Pheno-parenting

This dataset was collected with the goal of providing researchers with access to a collection of hundreds of images for efficient classification of plant attributes and multi-instance plant localisation and detection. There are two folders, i.e. Side view and Top View.Each folder includes label files and image files in the.jpg format (.txt format). Images of 30 plants grown in 5 hydroponic systems have been collected for 66 days. Thirty plants of three species (Petunia, Pansy and Calendula) were grown in a hydroponic system for the purpose of collecting and analysing images. Tasks like plant species recognition, health condition, growth stage and flowering stage identification can be performed using this soilless farming hydroponic farming dataset. For more details please refer to the following research article: Title: AI-Driven Pheno-Parenting: A Deep Learning Based Plant Phenotyping Trait Analysis Model on a Novel Soilless Farming DatasetDOI: 10.1109/ACCESS.2023.3265195citation: A. J. Hati and R. R. Singh, "AI-Driven Pheno-Parenting: A Deep Learning Based Plant Phenotyping Trait Analysis Model on a Novel Soilless Farming Dataset," in IEEE Access, vol. 11, pp. 35298-35314, 2023, doi: 10.1109/ACCESS.2023.3265195.

本数据集旨在为研究人员提供数百张图像的集合，以实现植物属性的高效分类，以及多实例植物定位与检测。数据集分为两个子目录，即侧面视图和顶部视图。每个子目录均包含标签文件和.jpg格式的图像文件（.txt格式）。收集了在五种水培系统中种植的30种植物的图像，持续观察了66天。为收集和分析图像，选取了三种物种（天竺葵、三色堇和金盏花）的30株植物在水培系统中进行培育。利用本无土栽培水培农业数据集，可执行植物物种识别、健康状况、生长阶段及开花阶段识别等任务。欲获取更多详细信息，请参阅以下研究论文：标题：AI驱动的表型育种：基于新型无土栽培数据集的深度学习植物表型性状分析模型DOI：10.1109/ACCESS.2023.3265195引用：A. J. Hati 和 R. R. Singh，"AI驱动的表型育种：基于新型无土栽培数据集的深度学习植物表型性状分析模型"，发表于IEEE Access，第11卷，第35298-35314页，2023年，doi：10.1109/ACCESS.2023.3265195。