Accurate machine learning-based germination detection, prediction and quality assessment of three grain crops
- PMID: 33353559
- PMCID: PMC7754596
- DOI: 10.1186/s13007-020-00699-x
Accurate machine learning-based germination detection, prediction and quality assessment of three grain crops
Abstract
Background: 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.
Results: We generated labeled imaging data of the germination process of more than 2400 seeds for three different crops, Zea mays (maize), Secale cereale (rye) and Pennisetum glaucum (pearl millet), with a total of more than 23,000 images. Different state-of-the-art convolutional neural network (CNN) architectures with region proposals have been trained using transfer learning to automatically identify seeds within petri dishes and to predict whether the seeds germinated or not. Our proposed models achieved a high mean average precision (mAP) on a hold-out test data set of approximately 97.9%, 94.2% and 94.3% for Zea mays, Secale cereale and Pennisetum glaucum respectively. Further, various single-value germination indices, such as Mean Germination Time and Germination Uncertainty, can be computed more accurately with the predictions of our proposed model compared to manual countings.
Conclusion: Our proposed machine learning-based method can help to speed up the assessment of seed germination experiments for different seed cultivars. It has lower error rates and a higher performance compared to conventional and manual methods, leading to more accurate germination indices and quality assessments of seeds.
Keywords: Faster R-CNN; Germination indices; Germination prediction; Machine learning; Seed germination.
Conflict of interest statement
SJS is Managing Director at Computomics GmbH. The other authors declare that they have no competing interests.
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References
-
- King T, Cole M, Farber JM, Eisenbrand G, Zabaras D, Fox EM, et al. Food safety for food security: Relationship between global megatrends and developments in food safety. Trends Food Sci Technol. 2017;68:160–175. doi: 10.1016/j.tifs.2017.08.014. - DOI
-
- Marcos Filho J, Marcos FJ. Seed vigor testing: an overview of the past, present and future perspective. Sci Agric Scientia Agricola. 2015;72:363–374. doi: 10.1590/0103-9016-2015-0007. - DOI
-
- ISTA The germination test. Int Rules Seed Test. 2015 doi: 10.15258/istarules.2015.05. - DOI
-
- Chaugule A. Application of image processing in seed technology: a survey. Int J Emerg Technol Adv Eng. 2012;2(4):153–159.
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