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Fig. 3 | Plant Methods

Fig. 3

From: Accurate machine learning-based germination detection, prediction and quality assessment of three grain crops

Fig. 3

Normalized confusion matrix of test sets in percent for Inception-ResNet v2. True germination state as rows, predicted state as columns for the respective Inception-ResNet v2 model. Germinated seeds are denoted as “g”, non-germinated as “ng” and seeds which are not localized or classified by the model are denoted as “bg” (background). Green: Correct classification of the seed germination state. Yellow: Misclassification of the germination state. Orange: Incorrect localization of background as a seed (incorrect region proposal) resulting in seeds being detected multiple times. Red: Incorrect detection of a seed as background resulting in less detections than seeds present in the petri dish. a Zea mays (8809 detected instances) with a classification error of 4.1% and localization error of 0.9%. b Secale cereale (8564 detected instances) with a classification error of 12.1% and a localization error of 0.4%. c Pennisetum glaucum (8826 detected instances) with a classification error of 9.1% and a localization error of 2.2%

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