Abstract：
The improper storage of seeds can potentially compromise agricultural productivity, leading to reduced crop yields. Therefore, assessing seed viability before sowing is of paramount importance. Although numerous techniques exist for evaluating seed conditions, this research leveraged hyperspectral imaging (HSI) technology as an innovative, rapid, clean, and precise nondestructive testing method. The study aimed to determine the most effective classification model for watermelon seeds. Initially, purchased watermelon seeds were segregated into two groups: One underwent sterilization in a dehydrator machine at 40°C for 36 h, whereas the other batch was stored under favorable conditions. Watermelon seeds\' spectral images were captured using an HSI with a charge-coupled device camera ranging from 400 to 1000 nm, and the segmented regions of all samples were measured. Preprocessing techniques and wavelength selection methods were applied to manage spectral data workload, followed by the implementation of a support vector machine (SVM) model. The initial hybrid-SVM model achieved a predictive accuracy rate of 100%, with a test set accuracy of 92.33%. Subsequently, an artificial bee colony (ABC) optimization was introduced to enhance model precision. The results indicated that, with kernel parameters (c, g) set at 13.17 and 0.01, respectively, and a runtime of 4.19328 s, the training and evaluation of the dataset achieved an accuracy rate of 100%. Hence, it was practical to utilize HSI technology combined with the PCA-ABC-SVM model to detect different watermelon seeds. As a result, these findings introduce a novel technique for accurately forecasting seed viability, intended for use in agricultural industrial multispectral imaging. PRACTICAL APPLICATION: The traditional methods for determining the condition of seeds primarily emphasize aesthetics, rely on subjective assessment, are time-consuming, and require a lot of labor. On the other hand, HSI technology as green technology was employed to alleviate the aforementioned problems. This work significantly contributes to the field of industrial multispectral imaging by enhancing the capacity to discern various types of seeds and agricultural crop products.
摘要：
种子储存不当可能会损害农业生产力，导致作物产量下降。因此,播种前评估种子活力至关重要。尽管存在许多评估种子条件的技术，这项研究利用高光谱成像(HSI)技术作为一项创新，快速,干净,和精确的无损检测方法。该研究旨在确定最有效的西瓜种子分类模型。最初，将购买的西瓜种子分为两组：一组在脱水机中在40°C下灭菌36小时，而另一批在有利的条件下储存。使用HSI和400至1000nm的电荷耦合器件相机捕获西瓜子的光谱图像，并测量所有样品的分割区域。应用预处理技术和波长选择方法来管理光谱数据工作量，其次是支持向量机(SVM)模型的实现。初始的混合SVM模型实现了100%的预测准确率,测试集精度为92.33%。随后，引入人工蜂群(ABC)优化模型以提高模型精度。结果表明,使用内核参数(c，g)分别设置为13.17和0.01，运行时间为4.19328s，数据集的训练和评估达到了100%的准确率。因此，利用HSI技术结合PCA-ABC-SVM模型检测不同的西瓜种子是实用的。因此,这些发现引入了一种准确预测种子活力的新技术，用于农业工业多光谱成像。实际应用：确定种子状况的传统方法主要强调美学，依靠主观评估，是耗时的，并且需要大量的劳动力。另一方面,采用HSI技术作为绿色技术来缓解上述问题。这项工作通过增强辨别各种类型的种子和农作物产品的能力，为工业多光谱成像领域做出了重大贡献。
