For the first time in literature, an analytical method was developed using artificial neural networks (ANNs) combined with color information from digital images to predict the content of sugarcane stalks in the presence of a solid impurity. The data were generated using a laboratory-made simple imaging system and free-access computational routine for the conversion of the images into 10 colors. The ANN model was implemented using 10 neurons in the input layer, 8 neurons in the hidden layer and 1 neuron in the output layer related to the content of sugarcane stalks. The ANN model provided relative errors of 3% and achieved correlation coefficients of 0.98, 0.93, and 0.91 for the training, validation and test sets, respectively. A partial least squares (PLS) model showed the nonlinear nature of the data that implies the application of ANN model. The developed method has the potential to be applied in sugarcane mills as an improvement for the production of high-quality sugar.

中文翻译：

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使用专注于制糖的人工智能方法在存在固体杂质的情况下预测甘蔗茎含量

文献中首次使用人工神经网络（ANN）结合数字图像中的颜色信息开发了一种分析方法，以预测存在固体杂质时甘蔗茎的含量。数据是使用实验室制造的简单成像系统和自由访问的计算例程生成的，用于将图像转换为10种颜色。ANN模型是通过使用与甘蔗茎的含量相关的输入层中的10个神经元，隐藏层中的8个神经元和输出层中的1个神经元来实现的。人工神经网络模型提供了3％的相对误差，并且在训练，验证和测试集上的相关系数分别为0.98、0.93和0.91。偏最小二乘（PLS）模型显示了数据的非线性性质，这意味着ANN模型的应用。