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Hydroponic farming plant life cycle dataset for AI driven Pheno-parenting
- Citation Author(s):
-
Anirban JyotiHati
Rajiv RanjanSingh - Submitted by:
- Anirban Hati
- Last updated:
- Fri, 04/21/2023 - 07:13
- DOI:
- 10.21227/fdk6-wc97
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- Research Article Link:
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Abstract
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 Dataset
DOI: 10.1109/ACCESS.2023.3265195
citation: 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.
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.
Two folders (Side view, top view) contains image files (.jpg format) and their label files (.txt format). The naming convention of the files are as follows:
Camera view (S or T)_Lighting condition (D or N)_Date (YYYYMMDD format)_time (24 hour system)
Where S, T, D and N indicates side view, top view, day time and night time respectively.
The dataset can be used for phenotyping task such as species recognition, plant health condition (Healthy or stressed), growth stages (young or mature), flowering stage (flowered or not flowered). Based on these taks 25 classes have been created. The file is also added in the folder.
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 Dataset
DOI: 10.1109/ACCESS.2023.3265195
citation: 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.
