It is crucial to identify and select high-quality seeds for improving Scutellaria baicalensis yield. In this study, we present a non-destructive and accurate method for predicting Scutellaria baicalensis seed viability that used seed phenotypic data with machine-learning algorithms to distinguish between vital and dead seeds. Meanwhile, the SMOTE was used to balance the dataset and make the established viability discrimination model more efficient by avoiding problems of overfitting or under-fitting. The results showed that hyperspectral imaging (HSI) combined with detrend (DT) preprocessing and a support vector machine (SVM) model could predict Scutellaria baicalensis seed viability with a 93.3% accuracy, and increased the germination percentage of the seed lot to 99.1%, while machine vision imaging provided the highest 87.9% accuracy and 87.0% germination percentage. This strategy is suitable for large-scale Scutellaria baicalensis seed viability discrimination operations for ensuring seed quality, expanding the cultivation and production scales of Scutellaria baicalensis, and accelerating the present solving of the problem of short supply. It can help to accelerate the breeding of quality Scutellaria baicalensis varieties.
Seed processing is an important means of improving seed quality. However, the traditional seed processing process and parameter adjustment are highly empirically dependent. In this study, machine vision technology was used to develop a seed processing method based on the rapid extraction of seeds’ material characteristics. Combined with the results of clarity analysis and the single seed germination test, the seed processing process and parameters were determined through data analysis. The results showed that several phenotypic features were significantly or highly significantly correlated with clarity, but fewer phenotypic features were correlated with viability. According to the probability density distribution of pure seeds and impurities in the features that were significantly correlated with seed clarity, the sorting parameters of length, width, R, G, and B were determined. When the combination of width (≥0.8 mm) + G (<75) was used for sorting, the recall of pure seeds was higher than 91%, and the precision was increased to 98.6%. Combined with the specific production reality, the preliminary determination of the Platycodon grandiflorum seed processing process was air separation—screen (round hole sieve)—color sorting. Then, four commercialized Platycodon grandiflorum seed lots were sorted by this process using corresponding parameters in the actual processing equipment. Subsequently, the seed clarity and germination percentage were significantly improved, and the seed quality qualification rate was increased from 25% to 75%. In summary, by using machine vision technology to quickly extract the material characteristics of the seeds, combined with correlation analysis, probability density distribution plots, single feature selection, and combination sorting comparisons, the appropriate processing process and corresponding sorting parameters for a specific seed lot can be determined, thus maximizing the seed quality.
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