Classification and differentiation of clean sugarcane from trash (green sugarcane leaf, dry sugarcane leaf, stone, and soil) are important for the sugar payment system at a sugar mill. Currently, the methods used to do this are manual and subjective. Therefore, this study is aimed at accurately differentiating clean sugarcane from trash by using hyperspectral imaging with multivariate analyses. Samples containing sugarcane billets and trash mixed in a ratio of 18:38 were analyzed in this study. The reflectance data of the samples were analyzed in the wavelength range of 400–1000 nm via principal component analysis (PCA). The PCA model was capable of identifying all of the clean sugarcane and trash samples. The spectral loadings of the PCA model show that the sugarcane and trash samples are easily identifiable based on the color (visible light) of each class, water absorption (approximately 970 nm), and chlorophyll absorption (approximately 680 nm). Based on the characteristic wavelengths of the PCA loading peaks, over 90% of the sugarcane and trash samples were differentiated using a multiple linear regression model. Sugarcane and trash are classified by using partial least-squares discriminant analysis and support vector machine models. For all wavelengths, the classification rate is 92.9% and 98.2%, respectively. This shows that sugarcane and trash can be accurately classified and differentiated by using hyperspectral imaging and multivariate analyses.

中文翻译：

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基于高光谱成像的制糖甘蔗和杂质识别系统的开发

从垃圾（绿色甘蔗叶、干甘蔗叶、石头和土壤）中分类和区分干净的甘蔗对于糖厂的糖支付系统很重要。目前，用于执行此操作的方法是手动和主观的。因此，本研究旨在通过使用高光谱成像和多变量分析来准确区分干净的甘蔗和垃圾。本研究分析了含有以 18:38 比例混合的甘蔗坯和垃圾的样品。通过主成分分析 (PCA) 在 400-1000 nm 的波长范围内分析样品的反射数据。PCA 模型能够识别所有干净的甘蔗和垃圾样本。PCA 模型的光谱载荷表明，根据每个类别的颜色（可见光）、吸水率（约 970 nm）和叶绿素吸收率（约 680 nm），可以轻松识别甘蔗和垃圾样品。基于 PCA 负载峰的特征波长，使用多元线性回归模型区分了超过 90% 的甘蔗和垃圾样品。使用偏最小二乘判别分析和支持向量机模型对甘蔗和垃圾进行分类。对于所有波长，分类率分别为 92.9% 和 98.2%。这表明使用高光谱成像和多变量分析可以准确地对甘蔗和垃圾进行分类和区分。和叶绿素吸收（约 680 nm）。基于 PCA 负载峰的特征波长，使用多元线性回归模型区分了超过 90% 的甘蔗和垃圾样品。使用偏最小二乘判别分析和支持向量机模型对甘蔗和垃圾进行分类。对于所有波长，分类率分别为 92.9% 和 98.2%。这表明使用高光谱成像和多变量分析可以准确地对甘蔗和垃圾进行分类和区分。和叶绿素吸收（约 680 nm）。基于 PCA 负载峰的特征波长，使用多元线性回归模型区分了超过 90% 的甘蔗和垃圾样品。使用偏最小二乘判别分析和支持向量机模型对甘蔗和垃圾进行分类。对于所有波长，分类率分别为 92.9% 和 98.2%。这表明使用高光谱成像和多变量分析可以准确地对甘蔗和垃圾进行分类和区分。使用偏最小二乘判别分析和支持向量机模型对甘蔗和垃圾进行分类。对于所有波长，分类率分别为 92.9% 和 98.2%。这表明使用高光谱成像和多变量分析可以准确地对甘蔗和垃圾进行分类和区分。使用偏最小二乘判别分析和支持向量机模型对甘蔗和垃圾进行分类。对于所有波长，分类率分别为 92.9% 和 98.2%。这表明使用高光谱成像和多变量分析可以准确地对甘蔗和垃圾进行分类和区分。