小米是一种潜力巨大的小种子谷类作物。谷子(PanicummiliaceumL.)有许多不同的品种,具有不同的特性,提出了对种植者很重要的分类问题,处理器,和消费者。当前的谷物品种检测和分类方法是主观的,破坏性,而且耗时。因此,有必要开发非破坏性的方法来分类proso小米的品种。在这项研究中,评估了使用近红外(NIR)高光谱成像(900-1700nm)区分不同品种的谷子种子的可行性。从美国十个最受欢迎的品种中随机获得并调查了总共5000个proso小米种子,即Cerise,应付,早期的鸟,亨斯迈,Minco,高原,起来,雪鸟,日出,和Sunup.为了降低高光谱成像的大维度,应用主成分分析(PCA),前两个主成分被用作建立分类模型的光谱特征,因为它们具有最大的方差。分类性能显示,使用梯度树增强集成机器学习算法对不同品种的谷子进行分类的预测准确率高达99%。此外,仅使用15和5个选定的光谱特征(波长)成功进行了分类,准确率分别为98.14%和97.6%,分别。总体结果表明,近红外高光谱成像可作为一种快速、无损的谷子种子分类方法。
Millet is a small-seeded cereal crop with big potential. There are many different cultivars of proso
millet (Panicum miliaceum L.) with different characteristics, bringing forth the issue of sorting which are important for growers, processors, and consumers. Current methods of grain cultivar detection and classification are subjective, destructive, and time-consuming. Therefore, there is a need to develop nondestructive methods for sorting the cultivars of proso
millet. In this study, the feasibility of using near-infrared (NIR) hyperspectral imaging (900-1700 nm) to discriminate between different cultivars of proso
millet seeds was evaluated. A total of 5000 proso
millet seeds were randomly obtained and investigated from the ten most popular cultivars in the United States, namely Cerise, Cope, Earlybird, Huntsman, Minco, Plateau, Rise, Snowbird, Sunrise, and Sunup. To reduce the large dimensionality of the hyperspectral imaging, principal component analysis (PCA) was applied, and the first two principal components were used as spectral features for building the classification models because they had the largest variance. The classification performance showed prediction accuracy rates as high as 99% for classifying the different cultivars of proso millet using a Gradient tree boosting ensemble machine learning algorithm. Moreover, the classification was successfully performed using only 15 and 5 selected spectral features (wavelengths), with an accuracy of 98.14% and 97.6%, respectively. The overall results indicate that NIR hyperspectral imaging could be used as a rapid and nondestructive method for the classification of proso millet seeds.