Data for: Accurate Machine Learning-Based Germination Detection, Prediction and Quality Assessment of Various Seed Cultivars

Assessment of seed germination is an essential task for seed researchers to measure the quality and performance of seeds. Usually, seed assessments are done mainly manual, which is a cumbersome, time consuming and error-prone process. Classical image analyses methods are not well suited for large-scale germination experiments, because they often rely on manual adjustments of color-based thresholds. We here propose a machine learning approach using modern artificial neural networks for accurate seed germination detection for high-throughput seed germination experiments.

This dataset contains captures of Germination Experiments of 824 Zea mays seeds, 811 Secale cereale seeds and 814 Pennisetum glaucum seeds. Approximately 10 seeds were placed in one petri dish and captured by a low-cost Raspberry Pi Camera Module (v2.1) in intervals of 30 minutes for ~ 2 days. All Images were annotated by Bounding Boxes containing their germination state (germinated/non-germinated). The code for running the models that are built with this data can be found on GitHub (https://github.com/grimmlab/GerminationPrediction).
Please cite our original publication if you have used the data in your project or in any follow-up analysis (https://doi.org/10.1186/s13007-020-00699-x):

@article{genze_accurate_2020,
title = {Accurate machine learning-based germination detection, prediction and quality assessment of three grain crops},
volume = {16},
issn = {1746-4811},
url = {https://doi.org/10.1186/s13007-020-00699-x},
doi = {10.1186/s13007-020-00699-x},
abstract = {Assessment of seed germination is an essential task for seed researchers to measure the quality and performance of seeds. Usually, seed assessments are done manually, which is a cumbersome, time consuming and error-prone process. Classical image analyses methods are not well suited for large-scale germination experiments, because they often rely on manual adjustments of color-based thresholds. We here propose a machine learning approach using modern artificial neural networks with region proposals for accurate seed germination detection and high-throughput seed germination experiments.},
number = {1},
journal = {Plant Methods},
author = {Genze, Nikita and Bharti, Richa and Grieb, Michael and Schultheiss, Sebastian J. and Grimm, Dominik G.},
month = dec,
year = {2020},
pages = {157},
}