Imanian Kamal, Pourdarbani Razieh, Sabzi Sajad, García-Mateos Ginés, Arribas Juan Ignacio, Molina-Martínez José Miguel
Department of Biosystems Engineering, College of Agriculture, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran.
Computer Science and Systems Department, University of Murcia, 30100 Murcia, Spain.
Foods. 2021 Apr 30;10(5):982. doi: 10.3390/foods10050982.
Potatoes are one of the most demanded products due to their richness in nutrients. However, the lack of attention to external and, especially, internal defects greatly reduces its marketability and makes it prone to a variety of diseases. The present study aims to identify healthy-looking potatoes but with internal defects. A visible (Vis), near-infrared (NIR), and short-wavelength infrared (SWIR) spectrometer was used to capture spectral data from the samples. Using a hybrid of artificial neural networks (ANN) and the cultural algorithm (CA), the wavelengths of 861, 883, and 998 nm in Vis/NIR region, and 1539, 1858, and 1896 nm in the SWIR region were selected as optimal. Then, the samples were classified into either healthy or defective class using an ensemble method consisting of four classifiers, namely hybrid ANN and imperialist competitive algorithm (ANN-ICA), hybrid ANN and harmony search algorithm (ANN-HS), linear discriminant analysis (LDA), and k-nearest neighbors (KNN), combined with the majority voting (MV) rule. The performance of the classifier was assessed using only the selected wavelengths and using all the spectral data. The total correct classification rates using all the spectral data were 96.3% and 86.1% in SWIR and Vis/NIR ranges, respectively, and using the optimal wavelengths 94.1% and 83.4% in SWIR and Vis/NIR, respectively. The statistical tests revealed that there are no significant differences between these datasets. Interestingly, the best results were obtained using only LDA, achieving 97.7% accuracy for the selected wavelengths in the SWIR spectral range.
土豆因其丰富的营养成分而成为最受欢迎的产品之一。然而，对外部缺陷，尤其是内部缺陷缺乏关注，大大降低了其市场适销性，并使其容易感染各种疾病。本研究旨在识别外观正常但有内部缺陷的土豆。使用可见（Vis）、近红外（NIR）和短波红外（SWIR）光谱仪从样品中采集光谱数据。采用人工神经网络（ANN）和文化算法（CA）的混合方法，选择Vis/NIR区域的861、883和998 nm波长，以及SWIR区域的1539、1858和1896 nm波长作为最优波长。然后，使用由四个分类器组成的集成方法将样品分为健康或有缺陷类别，这四个分类器分别是混合ANN和帝国主义竞争算法（ANN-ICA）、混合ANN和和声搜索算法（ANN-HS）、线性判别分析（LDA）和k近邻（KNN），并结合多数投票（MV）规则。使用仅选定的波长和所有光谱数据来评估分类器的性能。在SWIR和Vis/NIR范围内，使用所有光谱数据时的总正确分类率分别为96.3%和86.1%，使用最优波长时在SWIR和Vis/NIR中分别为94.1%和83.4%。统计检验表明，这些数据集之间没有显著差异。有趣的是，仅使用LDA获得了最佳结果，在SWIR光谱范围内选定波长的准确率达到了97.7%。
