Zhang Fu, Wang Mengyao, Zhang Fangyuan, Xiong Ying, Wang Xinyue, Ali Shaukat, Zhang Yakun, Fu Sanling
College of Agricultural Equipment Engineering Henan University of Science and Technology Luoyang China.
Collaborative Innovation Center of Advanced Manufacturing for Machinery and Equipment of Henan Province Luoyang China.
Food Sci Nutr. 2024 Apr 3;12(5):3177-3187. doi: 10.1002/fsn3.3984. eCollection 2024 May.
The demand for identification of maize varieties has increased dramatically due to the phenomenon of mixed seeds and inferior varieties pretending to be high-quality varieties continuing to occur. It is urgent to solve the problem of efficient and accurate identification of maize varieties. A hyperspectral image acquisition system was used to acquire images of maize seeds. Regions of interest (ROI) with an embryo size of 10 × 10 pixel were extracted, and the average spectral information in the range of 949.43-1709.49 nm was intercepted for the subsequent study in order to eliminate random noise at both ends. Savitzky-Golay (SG) smoothing algorithm and multiple scattering correction (MSC) were used to pretreat the full-band spectrum. The feature wavelengths were screened by successive projection algorithms (SPA), competitive adaptive reweighted sampling (CARS) single screening, and two combinations of CARS-SPA and CARS + SPA, respectively. Support vector machines (SVMs) and models optimized based on genetic algorithm (GA), particle swarm optimization (PSO) were established by using full bands (FB) and feature bands as the model input. The results showed that the MSC-(CARS-SPA)-GA-SVM model had the best performance with 93.00% of the test set accuracy, 8 feature variables, and a running time of 24.45 s. MSC pretreatment can effectively eliminate the scattering effect of spectral data, and the feature wavelengths extracted by CARS-SPA can represent all wavelength information. The study proved that hyperspectral imaging combined with GA-SVM can realize the identification of maize varieties, which provided a theoretical basis for maize variety classification and authenticity identification.
由于混种种子以及劣种冒充优质品种的现象不断发生，对玉米品种鉴定的需求急剧增加。解决玉米品种高效、准确鉴定的问题迫在眉睫。利用高光谱图像采集系统获取玉米种子图像。提取胚大小为10×10像素的感兴趣区域（ROI），截取949.43 - 1709.49nm范围内的平均光谱信息用于后续研究，以消除两端的随机噪声。采用Savitzky - Golay（SG）平滑算法和多元散射校正（MSC）对全波段光谱进行预处理。分别通过连续投影算法（SPA）、竞争性自适应重加权采样（CARS）单筛选以及CARS - SPA和CARS + SPA两种组合筛选特征波长。以全波段（FB）和特征波段作为模型输入，建立支持向量机（SVM）以及基于遗传算法（GA）、粒子群优化（PSO）优化的模型。结果表明，MSC -（CARS - SPA）- GA - SVM模型性能最佳，测试集准确率为93.00%，有8个特征变量，运行时间为24.45秒。MSC预处理能有效消除光谱数据的散射效应，CARS - SPA提取的特征波长能代表所有波长信息。该研究证明高光谱成像结合GA - SVM可实现玉米品种的鉴定，为玉米品种分类和真伪鉴定提供了理论依据。
