该研究聚焦于探讨利用多光谱成像系统结合化学计量法无损鉴别高品质西瓜种子的可行性。

研究使用了主成分分析法（PCA），较小二乘支持向量机(LS-SVM)，BP神经网络(BPNN),以及随机森林法（RF）来测定种子品质。

结果显示，光谱学和形态学特征在区分西瓜种子品质时非常重要。高品质西瓜种子与其它西瓜种子的显著区别，如死种子和低活力种子进行视觉化，区分度较好（Julong品种精度92%（LS-SVM）和Xiali品种91%（RF模型）。结果显示多光谱成像可用于快速、有效无损监测西瓜种子品质。

关键词

西瓜子，多光谱成像，无损

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Rapid Discrimination of High-Quality Watermelon Seeds by Multispectral Imaging Combined with Chemometric Methods

This study focuses on the feasibility of nondestructive discrimination of high-quality watermelon seeds with a multispectral imaging system combined with chemometrics. Principal component analysis (PCA), least squares-support vector machines (LS-SVM), back propagation neural network (BPNN), and random forest (RF) were applied to determine the seed quality. 

The results demonstrate that both the spectral and the morphological features are essential for discrimination of the quality of watermelon seeds. Clear differences between high-quality watermelon seeds and other watermelon seeds including dead seeds and low-vigor seeds were visualized, and an excellent classification (with accuracies of 92% in the LS-SVM model for Julong and 91% in the RF model for Xiali, respectively) was achieved. These results indicate that multispectral imaging could be used for rapid and efficient nondestructive quality control of watermelon seeds.

Keywords

watermelon seeds multispectral imaging nondestructive