Sabzi Sajad, Pourdarbani Razieh, Rohban Mohammad Hossein, Fuentes-Penna Alejandro, Hernández-Hernández José Luis, Hernández-Hernández Mario
Department of Biosystems Engineering, College of Agriculture, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran.
Computer Engineering Department, Sharif University of Technology, Tehran 14588-89694, Iran.
Plants (Basel). 2021 Apr 29;10(5):898. doi: 10.3390/plants10050898.
Improper usage of nitrogen in cucumber cultivation causes nitrate accumulation in the fruit and results in food poisoning in humans; therefore, mandatory evaluation of food products becomes inevitable. Hyperspectral imaging has a very good ability to evaluate the quality of fruits and vegetables in a non-destructive manner. The goal of the present paper was to identify excess nitrogen in cucumber plants. To obtain a reliable result, the majority voting method was used, which takes into account the unanimity of five classifiers, namely, the hybrid artificial neural network-imperialism competitive algorithm (ANN-ICA), the hybrid artificial neural network-harmonic search (ANN-HS) algorithm, linear discrimination analysis (LDA), the radial basis function network (RBF), and the K-nearest-neighborhood (KNN). The wavelengths of 723, 781, and 901 nm were determined as optimal wavelengths using the hybrid artificial neural network-biogeography-based optimization (ANN-BBO) algorithm, and the performance of classifiers was investigated using the optimal spectrum. The results of a -test showed that there was no significant difference in the precision of the algorithm when using the optimal wavelengths and wavelengths of the whole range. The correct classification rate of the classifiers ANN-ICA, ANN-HS, LDA, RBF, and KNN were 96.14%, 96.11%, 95.73%, 64.03%, and 95.24%, respectively. The correct classification rate of majority voting (MV) was 95.55% for test data in 200 iterations, which indicates the system was successful in distinguishing nitrogen-rich leaves from leaves with a standard content of nitrogen.
黄瓜种植中氮素使用不当会导致果实中硝酸盐积累，进而引发人类食物中毒；因此，对食品进行强制性评估变得不可避免。高光谱成像具有以非破坏性方式评估水果和蔬菜质量的良好能力。本文的目的是识别黄瓜植株中的过量氮素。为了获得可靠的结果，采用了多数投票法，该方法考虑了五个分类器的一致性，即混合人工神经网络-帝国主义竞争算法（ANN-ICA）、混合人工神经网络-和声搜索（ANN-HS）算法、线性判别分析（LDA）、径向基函数网络（RBF）和K近邻（KNN）。使用混合人工神经网络-基于生物地理学的优化（ANN-BBO）算法确定723、781和901 nm波长为最佳波长，并使用最佳光谱研究分类器的性能。t检验结果表明，使用最佳波长和全范围波长时算法的精度没有显著差异。分类器ANN-ICA、ANN-HS、LDA、RBF和KNN的正确分类率分别为96.14%、96.11%、95.73%、64.03%和95.24%。在200次迭代中，测试数据的多数投票（MV）正确分类率为95.55%，这表明该系统成功地将富氮叶片与标准氮含量的叶片区分开来。
