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Probing 1D convolutional neural network adapted to near-infrared spectroscopy for efficient classification of mixed fish
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy ( IF 4.3 ) Pub Date : 2022-05-16 , DOI: 10.1016/j.saa.2022.121350
Xinghao Chen 1 , Gongyi Cheng 2 , Shuhan Liu 2 , Sizhuo Meng 2 , Yiping Jiao 2 , Wenjie Zhang 2 , Jing Liang 2 , Wang Zhang 3 , Bin Wang 1 , Xiaoxuan Xu 1 , Jing Xu 1
Affiliation  

Salmon and Cod are economically significant world-class fish that have high economic value. It is difficult to accurately sort and process them by appearance during harvest and transportation. Conventional chemical detection means are time-consuming and costly, which greatly affects the cost and efficiency of Fishery production. Therefore, there is an urgent need for smart Fisheries methods which use for the classification of mixed fish. In this paper, near-infrared spectroscopy (NIRS) was used to assess salmon and cod samples. This study aims to evaluate feasibility of a back-propagation neural network (BPNN) and a convolutional neural network (CNN) for identifying different species of fishes by the corresponding spectra in comparison to traditional chemometrics Partial Least Squares. After comparing the effects of different batch sizes, number of convolutional kernels, number of convolutional layers, and number of pooling layers on the classification of NIRS spectra comparing different structures of one-dimensional (1D)–CNN, we propose the 1D-CNN-8 model that is most suitable for the classification of mixed fish. Compared with the results of traditional chemometrics methods and BPNN, the prediction model of the 1D-CNN model can reach 98.00% Accuracy and the parameters are significantly better than others. Meanwhile, the parameters and floating-point operations of the optimal model are both small. Therefore, the improved CNN model based on the NIRS can effectively and quickly identify different kinds of fish samples and contribute to realizing edge computing at the same time.



中文翻译:

探索适用于近红外光谱的一维卷积神经网络,用于混合鱼的有效分类

三文鱼和鳕鱼是具有重要经济价值的世界级鱼类。在收获和运输过程中,很难通过外观准确分类和加工。常规的化学检测手段费时费力,极大地影响了渔业生产的成本和效率。因此,迫切需要用于混合鱼分类的智能渔业方法。在本文中,近红外光谱 (NIRS) 用于评估鲑鱼和鳕鱼样品。本研究旨在评估反向传播神经网络 (BPNN) 和卷积神经网络 (CNN) 与传统化学计量学偏最小二乘法相比,通过相应光谱识别不同鱼类的可行性。在比较不同batch size的效果后,卷积核数、卷积层数和池化层数对 NIRS 光谱的分类比较一维(1D)-CNN 的不同结构,我们提出了最适合分类的 1D-CNN-8 模型混合鱼。与传统化学计量学方法和 BPNN 的结果相比,1D-CNN 模型的预测模型可以达到 98.00% 的准确率,并且参数明显优于其他模型。同时,最优模型的参数和浮点运算都很小。因此,改进的基于近红外光谱的CNN模型可以有效、快速地识别不同种类的鱼类样本,同时有助于实现边缘计算。和池化层数对 NIRS 光谱的分类比较一维 (1D)-CNN 的不同结构,我们提出了最适合混合鱼分类的 1D-CNN-8 模型。与传统化学计量学方法和 BPNN 的结果相比,1D-CNN 模型的预测模型可以达到 98.00% 的准确率,并且参数明显优于其他模型。同时,最优模型的参数和浮点运算都很小。因此,改进的基于近红外光谱的CNN模型可以有效、快速地识别不同种类的鱼类样本,同时有助于实现边缘计算。和池化层数对 NIRS 光谱的分类比较一维 (1D)-CNN 的不同结构,我们提出了最适合混合鱼分类的 1D-CNN-8 模型。与传统化学计量学方法和 BPNN 的结果相比,1D-CNN 模型的预测模型可以达到 98.00% 的准确率,并且参数明显优于其他模型。同时,最优模型的参数和浮点运算都很小。因此,改进的基于近红外光谱的CNN模型可以有效、快速地识别不同种类的鱼类样本,同时有助于实现边缘计算。与传统化学计量学方法和 BPNN 的结果相比,1D-CNN 模型的预测模型可以达到 98.00% 的准确率,并且参数明显优于其他模型。同时,最优模型的参数和浮点运算都很小。因此,改进的基于近红外光谱的CNN模型可以有效、快速地识别不同种类的鱼类样本,同时有助于实现边缘计算。与传统化学计量学方法和 BPNN 的结果相比,1D-CNN 模型的预测模型可以达到 98.00% 的准确率,并且参数明显优于其他模型。同时,最优模型的参数和浮点运算都很小。因此,改进的基于近红外光谱的CNN模型可以有效、快速地识别不同种类的鱼类样本,同时有助于实现边缘计算。

更新日期:2022-05-16
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