Table_7_Integrating optical imaging techniques for a novel approach to evaluate Siberian wild rye seed maturity.docx

Advances in optical imaging technology using rapid and non-destructive methods have led to improvements in the efficiency of seed quality detection. Accurately timing the harvest is crucial for maximizing the yield of higher-quality Siberian wild rye seeds by minimizing excessive shattering during harvesting. This research applied integrated optical imaging techniques and machine learning algorithms to develop different models for classifying Siberian wild rye seeds based on different maturity stages and grain positions. The multi-source fusion of morphological, multispectral, and autofluorescence data provided more comprehensive information but also increases the performance requirements of the equipment. Therefore, we employed three filtering algorithms, namely minimal joint mutual information maximization (JMIM), information gain, and Gini impurity, and set up two control methods (feature union and no-filtering) to assess the impact of retaining only 20% of the features on the model performance. Both JMIM and information gain revealed autofluorescence and morphological features (CIELab A, CIELab B, hue and saturation), with these two filtering algorithms showing shorter run times. Furthermore, a strong correlation was observed between shoot length and morphological and autofluorescence spectral features. Machine learning models based on linear discriminant analysis (LDA), random forests (RF) and support vector machines (SVM) showed high performance (>0.78 accuracies) in classifying seeds at different maturity stages. Furthermore, it was found that there was considerable variation in the different grain positions at the maturity stage, and the K-means approach was used to improve the model performance by 5.8%-9.24%. In conclusion, our study demonstrated that feature filtering algorithms combined with machine learning algorithms offer high performance and low cost in identifying seed maturity stages and that the application of k-means techniques for inconsistent maturity improves classification accuracy. Therefore, this technique could be employed classification of seed maturity and superior physiological quality for Siberian wild rye seeds.

基于快速无损检测的光学成像技术进展，推动了种子品质检测效率的提升。精准确定收获时机，可通过最小化收获过程中的过度落粒，最大化高品质西伯利亚野黑麦（Siberian wild rye）种子的产量，这一点至关重要。本研究结合光学成像技术与机器学习算法，针对不同成熟阶段与籽粒着生位置的西伯利亚野黑麦种子，构建了多种分类模型。形态学、多光谱与自发荧光（autofluorescence）数据的多源融合可提供更全面的信息，但同时也提升了设备的性能要求。为此，本研究采用了三种特征筛选算法：最小联合互信息最大化（minimal joint mutual information maximization, JMIM）、信息增益（information gain）以及基尼不纯度（Gini impurity），并设置了两种对照方法（特征合并与无筛选），以评估仅保留20%特征对模型性能的影响。JMIM与信息增益算法均筛选出了自发荧光与形态学特征（CIELab A、CIELab B、色调与饱和度），且这两种筛选算法的运行时长更短。此外，本研究发现芽长与形态学及自发荧光光谱特征之间存在显著相关性。基于线性判别分析（linear discriminant analysis, LDA）、随机森林（random forests, RF）与支持向量机（support vector machines, SVM）的机器学习模型，在不同成熟阶段种子的分类任务中均表现出优异性能（准确率＞0.78）。进一步研究发现，同一成熟阶段下不同籽粒着生位置的种子存在显著差异，通过K-means（K均值）聚类方法可将模型性能提升5.8%~9.24%。综上，本研究证实：将特征筛选算法与机器学习算法相结合，可在种子成熟阶段识别任务中实现高性能与低成本；而针对成熟度不一致的种子应用K-means聚类技术，能够进一步提升分类准确率。因此，该技术可应用于西伯利亚野黑麦种子的成熟度分类及优良生理品质鉴定。