| 基于气敏传感器阵列特征优化的储粮害虫赤拟谷盗检测 |
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| 引用本文： | 张硕,韩少云,熊黎剑,侯宇鑫,高薪,汤修映.基于气敏传感器阵列特征优化的储粮害虫赤拟谷盗检测[J].农业工程学报,2022,38(10):303-309. |
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| 作者姓名： | 张硕 韩少云 熊黎剑 侯宇鑫 高薪 汤修映 |
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| 作者单位： | 中国农业大学工学院,北京 100083 |
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| 基金项目： | 国家重点研发计划"粮油质量安全过程保障与追溯技术集成与示范支持"项目（2019YFC1605303） |
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| 摘 要： | 为实现储粮中害虫赤拟谷盗（Tribolium castaneum（Herbst））的检测,该研究使用自主开发的储粮害虫电子鼻检测装置,采集了小麦中不同虫口密度梯度的赤拟谷盗挥发性气味信息,根据10个气敏传感器采集到的响应曲线,提取了各个传感器的相对变化值（Relative Change,RC）、相对积分值（Relative Integral,RI）、平均微分值（Mean Difference,MD）作为原始特征矩阵（10×3）,使用遗传算法（Genetic Algorithm,GA）作为特征选择方法,获得样本的特征信息,通过建立预测回归模型,实现了对小麦中赤拟谷盗虫口密度的预测。以识别准确率作为评价指标,对原始的特征矩阵进行了多特征优化,优化后的特征矩阵的识别准确率由原始的82.85% 提升至97.14%,优化后的特征数量由原始的30个减少为12个,特征数量减少60%,传感器数量减少至8个。最后通过采用偏最小二乘回归（Partial Least Squares Regression,PLSR）、主成分回归（Principal Components Regression,PCR）和支持向量机回归（Support Vector Machine Regression,SVR）3种回归方法进行回归预测,研究结果表明：基于偏最小二乘回归（PLSR）的预测模型达到了较好的预测效果,预测集回归模型的相关系数r和均方根误差RMSE分别为0.828和11.293。研究证明了气敏传感器阵列多特征优化方法的可行性和有效性,同时为实现粮食虫害快检测提供一种方法和参考。
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| 关 键 词： | 传感器 优化 电子鼻 粮食虫害 挥发性气味 赤拟谷盗 传感器阵列 特征选择 预测 |
| 收稿时间： | 2022/2/6 0:00:00 |
| 修稿时间： | 2022/4/10 0:00:00 |
Detection of stored grain pests Tribolium castaneum (Herbst) based on the feature optimization of gas sensor array |
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| Zhang Shuo,Han Shaoyun,Xiong Lijian,Hou Yuxin,Gao Xin,Tang Xiuying.Detection of stored grain pests Tribolium castaneum (Herbst) based on the feature optimization of gas sensor array[J].Transactions of the Chinese Society of Agricultural Engineering,2022,38(10):303-309. |
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| Authors: | Zhang Shuo Han Shaoyun Xiong Lijian Hou Yuxin Gao Xin Tang Xiuying |
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| Institution: | College of Engineering, China Agricultural University, Beijing 100083, China |
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| Abstract: | Abstract: Tribolium castaneum (Herbst) has been one of the most destructive insects for cereals and products in the world. A rapid and accurate detection can be greatly contributed to the stored grain in recent years. In this study, an electronic nose detection device was developed to detect the pests in stored wheat grains using the feature optimization of the gas sensor array. The volatile odor information of Herbst in wheat was also collected with different population density gradients. Firstly, the response signal of the gas sensor array was analyzed and preprocessed after collection. Then, the feature extraction and selection were performed on the response curve of the sensor array, in order to obtain the feature information of the sample. A regression model was established to predict the population density of Herbst in wheat. The Relative Change value (RC), Relative Integral value (RI), and Mean Differential value (MD) of each sensor were extracted as the original feature matrix (10×3), according to the response curves collected by 10 gas sensors. A Genetic Algorithm (GA) was adopted as the feature selection to optimize the original multi-feature matrix, where the recognition accuracy was used as the evaluation index. Finally, Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), and Support Vector Regression (SVR) were used for the regression prediction. The results show that the PLSR model achieved the best prediction. The Principal Component Analysis (PCA) and Cluster Analysis (CA) were used to analyze the discrimination and aggregation between different types of samples. It was found that the electronic nose device effectively distinguished the wheat samples from those infected by pests. The redundant information was removed from the optimized feature matrix, according to the recognition accuracy and PCA data. The recognition accuracy of the feature matrix increased from 82.85% to 97.14% after GA optimization. The number of feature variables was reduced by 60% from 30 to 12, and the number of sensors was reduced to 8. The PLSR, PCR, and SVR were used to establish the prediction model before and after the optimization of the matrix. The prediction set correlation coefficient (r) and root mean square error (RMSE) of the PLSR model were 0.828 and 11.293, respectively. By contrast, the prediction set r and RMSE of the PCR model were 0.764 and 13.859, respectively. The prediction set r and RMSE of the SVR model were 0.804 and 12.976, respectively. The results showed that the PLSR was the best model for the prediction of the population density of Herbst. Consequently, the feasibility and effectiveness of the sensor array multi-feature optimization can be verified for the electronic nose detection of stored grain pests. The finding can also provide a strong reference for the rapid detection of grain pests. |
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| Keywords: | sensors optimization electronic nose food pests volatile odor Tribolium castaneum (Herbst) sensor array feature selection prediction |
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