基于高光谱成像法对大豆种子的品种分类鉴别

Variety Classification and Identification of Soybean Seeds Based on Hyperspectral Imaging Method

本文针对东北大豆品种分类鉴别的需求,利用高光谱成像技术获取了6种大豆样品在392.38~1011.01 nm高光谱图像,提取感兴趣(ROI)区域数据,获得大豆种子样品的反射光谱曲线。经过卷积平滑(S-G)预处理,再根据大豆光谱曲线差异选取455.54 nm、479.3 nm、604.04 nm、657.46 nm、705.72 nm、856.89 nm、918.07 nm、953.54 nm作为特征波段,分别输入极限学习机(ELM)和随机森林(RF)模型,得到的分类正确率分别为78.22% 和98.89%,模型预测时间分别为11 s和12 s。研究结果表明,经卷积平滑和高光谱波段优化的特征波段,运用随机森林(RF)模型分析是分类准确率最高、预测时间较快的分类方法,高光谱成像法可有效对大豆品种做出分类鉴别。

According to the needs of soybeanvariety classification and identification in Northeast China, the hyperspectralimages of six soybean samples at 392.38~1011.01 nm were obtained byhyperspectral imaging technology, the region of interest (ROI) data wereextracted, and the reflection spectrum curves of soybean samples were obtained.After convolution smoothing (S-G) pretreatment, 455.54 nm, 479.3 nm, 604.04 nm,657.46 nm, 705.72 nm, 856.89 nm, 918.07 nm and 953.54 nm were selected as thecharacteristic bands according to the difference of soybean spectral curves.The classification accuracy was 78.22% and 98.89% respectively, and theprediction time of the model was 11 s and 12 s respectively. The results showthat the random forest (RF) model analysis is the classification method withthe highest classification accuracy and faster prediction time. Hyperspectralimaging method can effectively classify and identify soybean varieties.