Computer vision (CV) techniques help to perform non-destructive seed viability detection (SVD) for faster, more efficient and fairer results. However, the seed vigor dataset currently suffers from insufficient number of samples, data noise, and imbalance of positive and negative samples. In order to compensate for the shortcomings of the dataset, we created a maize seed germination dataset with multi-labeled classes and sufficient sample size, which helps in modeling seed germination prediction, seed viability classification, seed viability detection, and seed germination counting.
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