Non-Destructive Viability Discrimination for Individual Scutellaria baicalensis Seeds Based on High-Throughput Phenotyping and Machine Learning

Abstract

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.