NIAB teff phenotyping platform
NIAB teff phenotyping platform
Datasets
NIAB teff phenotyping platform
File
NIAB teff phenotyping platform
Use Case
NIAB teff phenotyping platform
Description
This dataset comprises 1120 high-resolution RGB images from a top-down view of pots containing teff seeds, captured at the National Institute of Agricultural Botany (NIAB). The images monitor teff root and shoot growth over time, with seeds arranged around the perimeter of transparent pots.
Description:
The dataset consists of 1120 high-resolution RGB images, all taken from a top-down perspective of various pots. These images were obtained from a phenotyping platform developed at the National Institute of Agricultural Botany (NIAB) to analyze teff roots and shoots. Each pot contained 16 seeds from different mutant teff strains to study their growth in a shared environment. The seeds were arranged around the perimeter of transparent pots to allow for side-view imaging of root growth, although these specific images are not included in this dataset.
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The pots were divided into four groups, each containing 40 pots. Each group, or block, is labeled as EXP{X}_Block{Y}. The genotypes were duplicated in each block and placed on a bench with varying airflow gradients. Photos were taken every two to three days to monitor plant development, resulting in a series of seven images per pot. Each day’s images are stored in separate directories per block, with the first directory containing the earliest images and the final directory containing the latest images.
Dataset Structure and Organization
The dataset is systematically organized to support efficient data handling and model training workflows. Each block (EXP{X}_Block{Y}) contains time-series images stored in sequential directories, representing different stages of plant growth. This structured format enables researchers to easily track temporal changes and analyze plant development over time.
Additionally, the separation of images by day and block allows for flexible dataset splitting, making it easier to create training, validation, and testing sets for machine learning models.
Key Features of the Dataset
- High-Resolution Imaging: All images are captured in RGB format with high clarity, ensuring detailed visualization of plant growth patterns.
- Time-Series Data: Images captured at regular intervals provide valuable sequential data for growth analysis.
- Controlled Experimental Setup: The dataset is collected under controlled conditions, ensuring consistency and reliability.
- Multi-Genotype Representation: Includes multiple teff strains, allowing comparative analysis across different genotypes.
Potential Applications
- Plant Growth Monitoring: Analyze growth patterns and development stages over time.
- Phenotyping Research: Study plant traits and genotype performance under controlled conditions.
- Machine Learning Models: Train models for plant detection, growth prediction, and classification tasks.
- Agricultural Innovation: Support precision agriculture and automated crop monitoring systems.
Conclusion
This dataset provides a strong foundation for developing machine learning models for plant growth analysis and phenotyping. It supports applications in agricultural research, crop improvement, and AI-driven plant monitoring systems. With its structured and time-series data, it enables accurate and reliable analysis of plant development.
This dataset is sourced from Kaggle.
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