基于改进离散粒子群算法的青贮玉米原料含水率高光谱检测

快速、无损和准确检测青贮玉米原料含水率,对确保青贮玉米发酵品质、推动青贮产业健康快速发展有重要现实意义。为探究高光谱技术在青贮玉米原料含水率检测方面的可行性,研究通过高光谱成像系统获取青贮玉米原料高光谱图像并利用烘箱加热法测定实际含水率。在粒子更新方式和惯性权重2个方面对传统离散粒子群算法(discretebinary particle swarm optimization,DBPSO)进行优化,提出基于改进型离散粒子群算法(modified discrete binary particle swarm optimization,MDBPSO)的特征波段优选方法,并利用相关系数分析法(correlation coefficient,CC)、DBPSO和MDBPSO法提取原料含水率高光谱特征变量,基于全波段反射光谱(total spectral reflectance,TSR)和特征波段反射光谱建立青贮玉米原料含水率预测模型。结果表明,MDBPSO优选特征波段适应度函数的收敛精度和收敛效率较DBPSO法均有明显改善,最优适应度值由0.761 6提高至0.812 3,函数收敛迭代次数由280次降低至79次。MDBPSO-PLSR预测模型的建模精度和预测精度均高于CC-PLSR、DBPSO-PLSR和TSR-PLSR预测模型,其校正集决定系数Rc2和均方根误差RMSEC(root mean square error of calibration)分别为0.81和0.032,预测集决定系数Rp2和均方根误差RMSEP(root mean square error of prediction)分别为0.80和0.045。该研究表明,利用高光谱图像技术检测青贮玉米原料含水率具有较高的精度,研究可为后续开发青贮玉米原料水分快速检测仪器提供借鉴方法。

Moisture content dectection in silage maize raw material based on hyperspectrum and improved discrete particle swarm

Abstract: Moisture content of silage maize raw material affects juice discharge, compaction degree and microbial activity during the whole silage process, and it has further influence on silage fermentation quality. Rapid, non-destructive and accurate detection of moisture content in silage maize raw material is significant for ensuring the silage maize quality and promoting the silage industry healthy and rapidly. Hyperspectral images of silage maize raw material in the visible and near infrared (383-1 004 nm) regions were acquired by the hyperspectral imaging system, and then corresponding moisture content in silage maize raw material were obtained by oven heating method successfully. The hyperspectral information was extracted from the images by selecting the region of interest (ROI) using the ENVI software. The standard normalized variate (SNV) was applied for eliminating or weakening the effect of particle scattering on original hyperspectral data. The hyperspectral imaging provides much more information including spectral and image information for all the samples of silage maize raw material, however, hyperspectral imagery contains more noise and redundancy. These disturbances made it difficult to meet the needs of fast and effective detection of certain objects. Therefore, it was difficult to apply online industrial applications in daily life directly, and the feature band effective selection for hyperspectral images was very critical. In view of the disadvantages as poor efficiency and easy premature, the traditional discrete particle swarm optimization (DBPSO) was optimized in terms of particle updating method and inertia weight. A modified discrete particle swarm optimization (MDBPSO) was proposed to extract the hyperspectral feature bands effectively. The hyperspectral characteristic variables of raw material moisture content were extracted using the correlation coefficient (CC), DBPSO and MDBPSO method. Partial least squares regression (PLSR) prediction model for silage maize moisture content was established by using full band and characteristic band. The results indicated that the convergence accuracy and efficiency of MDBPSO had a significantly improvement compared with the DBPSO method. When the population number was 40 and the program independent test ran 20 times, for DBPSO, the maximum value of optimal fitness (OFVmax), the minimum value of optimal fitness (OFVmin), and the mean value of optimal fitness (OFVave) were 0.761 6, 0.680 4 and 0.731 8 respectively, and the number of iterations corresponding to the OFVmax was 280 times. The OFVmax, OFVmin, and OFVave were 0.812 3, 0.711 2 and 0.752 2 for MDBPSO, respectively, and the number of iterations corresponding to the OFVmax was 79 times. After the improvement of DBPSO method, OFV of the fitness function was increased from 0.761 6 to 0.812 3, the number of iterations was reduced from 280 to 79, and the convergence efficiency was increased by 71.79%. 188 and 62 eigenvectors were extracted by DBPSO and MDBPSO respectively. The characteristic bands selected by the DBPSO method were mainly distributed in 421-520 nm, followed by 571-670 nm and 871-920 nm, and the number of bands was 51, 45 and 15 respectively. The characteristic bands selected by the MDBPSO method were also mainly distributed in the above band, and the number of the wave segments was 15, 11 and 12 respectively. It could be inferred that the sensitive bands of moisture content of silage maize in visible light region are 421-520, 571-670 nm and 871-920 nm in near infrared region. Comparing the performance of the 4 models, the fitting accuracies of TSR-PLSR and CC-PLSR were lower, and the verification set determination coefficients (Rc2) were 0.69 and 0.70 respectively, and the prediction set determination coefficients (Rp2) were 0.67 and 0.64, respectively. The DBPSO-PLSR model was improved significantly, and the Rc2 and Rp2 was 0.76 and 0.76 respectively. The DBPSO-PLSR model performed better than the other 3 model: TSR-PLSR, CC-PLSR and DBPSO-PLSR, achieving the highest accuracy with Rc2 of 0.81, RMSEC of 0.032, Rp2 of 0.80, RMSEP of 0.045. The study demonstrated that the application of hyperspectral image technology to the nondestructive testing of the moisture content of silage maize raw material content had high feasibility, and could provide efficient guidance for rapid detecting instrument development.