Die chronologische Liste zeigt aktuelle Veröffentlichungen aus dem Forschungsbetrieb der Hochschule Weihenstephan-Triesdorf. Zuständig ist das Zentrum für Forschung und Wissenstransfer (ZFW).
8 Ergebnisse
Prof. Dr. Volker Zahner,
Markus Schmidbauer,
Gerhard Schwab,
Christof Angst
Esther Sanyé-Mengual,
Kathrin Specht,
Jan Vavra,
Prof. Dr. Martina Artmann,
Francesco Orsini,
Giorgio Gianquinto
Berechtigungen: Open Access
Berechtigungen: Peer Reviewed
Ecosystem Services of Urban Agriculture: Perceptions of Project Leaders, Stakeholders and the General Public (2020) Sustainability 12 (24).
DOI: 10.3390/su122410446
Dr. Sebastian J. Schultheiss,
Prof. Dr. Dominik Grimm,
Nikita Genze,
Richa Bharti,
Michael Grieb
BackgroundAssessment 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.ResultsWe 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 cerealeand 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.ConclusionOur 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.
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