College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun 130022, China.
College of Electronic and Information Engineering, Beihua University, Jilin 132021, China.
Sensors (Basel). 2024 Mar 14;24(6):1855. doi: 10.3390/s24061855.
The moisture content of corn seeds is a crucial indicator for evaluating seed quality and is also a fundamental aspect of grain testing. In this experiment, 80 corn samples of various varieties were selected and their moisture content was determined using the direct drying method. The hyperspectral imaging system was employed to capture the spectral images of corn seeds within the wavelength range of 1100-2498 nm. By utilizing seven preprocessing techniques, including moving average, S-G smoothing, baseline, normalization, SNV, MSC, and detrending, we preprocessed the spectral data and then established a PLSR model for comparison. The results show that the model established using the normalization preprocessing method has the best prediction performance. To remove spectral redundancy and simplify the prediction model, we utilized SPA, CASR, and UVE algorithms to extract feature wavelengths. Based on three algorithms (PLSR, PCR, and SVM), we constructed 12 predictive models. Upon evaluating these models, it was determined that the normalization-SPA-PLSR algorithm produced the most accurate prediction. This model boasts high RC2 and RP2 values of 0.9917 and 0.9914, respectively, along with low RMSEP and RMSECV values of 0.0343 and 0.0257, respectively, indicating its exceptional stability and predictive capabilities. This suggests that the model can precisely estimate the moisture content of maize seeds. The results showed that hyperspectral imaging technology provides technical support for rapid and non-destructive prediction of corn seed moisture content and new methods in seed quality evaluation.
玉米种子的水分含量是评价种子质量的重要指标，也是粮食检验的基本内容。本实验选用 80 份不同品种的玉米样品，采用直接干燥法测定其水分含量。利用 1100-2498nm 波长范围内的高光谱成像系统获取玉米种子的光谱图像。采用移动平均、S-G 平滑、基线、归一化、SNV、MSC 和去趋势化等 7 种预处理方法对光谱数据进行预处理，然后建立 PLSR 模型进行比较。结果表明，采用归一化预处理方法建立的模型具有最好的预测性能。为了去除光谱冗余，简化预测模型，采用 SPA、CASR 和 UVE 算法提取特征波长。基于 PLSR、PCR 和 SVM 三种算法，构建了 12 个预测模型。对这些模型进行评价，确定归一化-SPA-PLSR 算法具有最高的预测精度。该模型的 RC2 和 RP2 值分别为 0.9917 和 0.9914，RMSEP 和 RMSECV 值分别为 0.0343 和 0.0257，表明其具有较好的稳定性和预测能力。这表明该模型可以准确地估计玉米种子的水分含量。结果表明，高光谱成像技术为玉米种子水分含量的快速无损预测和种子质量评价提供了新的方法和技术支持。
