Accurate machine learning-based germination detection, prediction and quality assessment of three grain crops
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Plant Methods Open Access
METHODOLOGY
Accurate machine learning‑based germination detection, prediction and quality assessment of three grain crops Nikita Genze1,2, Richa Bharti1,2, Michael Grieb4, Sebastian J. Schultheiss5 and Dominik G. Grimm1,2,3*
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: Seed germination, Germination prediction, Germination indices, Machine learning, Faster R-CNN Background Seeds are essential for human society as a food source and serve as starting material for crops. The yield of crops is not only highly dependent on environmental factors but also on the quality of the seed. Therefore, assessment of seed germination is an essential task for seed *Correspondence: [email protected] 1 Technical University of Munich, TUM Campus Straubing for Biotechnology and Sustainability, Bioinformatics, Schulgasse 22, 94315 Straubing, Germany Full list of author information is available at the end of the article
researchers to measure the performance of different seed lots in order to improve the efficiency of food chains [1]. In fact it has become imperative as the global crop production must be doubled in order to supply a rising population by 2050 [2]. Conventional seed testing measures, especially seed vigor tests, are no
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