| 电子鼻技术在棉花早期棉铃虫虫害检测中的应用 |
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| 引用本文： | 代雨婷,周博,王俊.电子鼻技术在棉花早期棉铃虫虫害检测中的应用[J].农业工程学报,2020,36(3):313-320. |
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| 作者姓名： | 代雨婷 周博 王俊 |
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| 作者单位： | 浙江大学生物系统工程与食品科学学院,杭州,310058;盐城工学院机械工程学院,盐城,224051 |
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| 基金项目： | 国家自然科学基金资助项目（31671583） |
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| 摘 要： | 为了更好地获取棉花虫害信息,该文使用电子鼻和气质联用技术对受到不同数量棉铃虫早期危害的棉花进行检测。基于气质联用技术获得了棉花挥发物的成分和含量,基于电子鼻响应曲线提取了稳定值、面积值、平均微分值、小波能量值和多项式拟合曲线参数值5种特征值,筛选出3种较优单特征:稳定值、平均微分值和面积值,之后基于多特征分别使用多层感知神经网络、径向基函数神经网络和极限学习机3种神经网络方法进行分类分析。最后采用支持向量机回归分别基于3种较优单特征及多特征对危害棉花的棉铃虫数量进行回归预测。结果表明:多特征的分类效果优于单特征,基于多特征“稳定值和平均微分值”和极限学习机分类效果最好,训练集和测试集的分类正确率均达到100%。多特征的预测能力优于单特征,基于多特征“面积值和平均微分值”的回归模型预测效果最佳,训练集回归模型的决定系数(R^2)和均方根误差(RMSE)分别为0.9940和0.0860,测试集回归模型的R^2和RMSE分别为0.9230和0.3709,电子鼻对棉花早期棉铃虫虫害具有较好的区分和预测能力,电子鼻在棉花早期棉铃虫虫害中的检测具有一定的应用潜力。
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| 关 键 词： | 电子鼻 神经网络 预测 棉花 棉铃虫 特征选择 |
| 收稿时间： | 2019/9/26 0:00:00 |
| 修稿时间： | 2020/1/30 0:00:00 |
Application of electronic nose in detection of cotton bollworm infestation at an early stage |
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| Dai Yuting,Zhou Bo and Wang Jun.Application of electronic nose in detection of cotton bollworm infestation at an early stage[J].Transactions of the Chinese Society of Agricultural Engineering,2020,36(3):313-320. |
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| Authors: | Dai Yuting Zhou Bo and Wang Jun |
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| Institution: | 1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China;,2. Department of Mechanical Engineering, Yancheng Institute of Technology, Yancheng 224051, China; and 1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; |
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| Abstract: | Cotton bollborm is one of the main pests of cotton. Cotton is under threat of yield loss and poor quality because of the cotton bollworm. However, cotton bolworms tend to hide in the cotton plants so that there are limitations for conventional detection methods, such as acoustic signal method, image recognition method and spectral imaging technology. A lot of researches have shown that volatile organic compounds (VOCs) released by plants will change when they are attacked by pests. So it is possible to get the cotton bollworm damage information by detecting the volatiles. Currently, gas chromatograph-mass spectrometer (GC-MS) can accurately detect the composition and content of volatile matter. However, this method has some disadvantages in practical application, such as time-consuming, high cost and inconvenience. The electronic nose is composed of sensor array, which is an instrument to analyze, identify and detect most of the volatiles. In this study, electronic nose was used to detect the cotton plants infested with cotton bollworm of different amounts at an early stage. The volatile organic compounds (VOCs) in cotton were analyzed by GC-MS. The plant height of cotton used in the study was 50-70 cm, and the boll period was about 12 weeks. Cotton bollworms used in the study were at second-instar. The VOCs emitted by the undamaged and damaged cotton plants detected by GC-MS were different, which indicated that electronic nose had potential in the application of cotton bollworm detection. The curve of electronic nose sensor was obtained for cotton plants infected by different numbers of cotton bollworm. Then five kinds of feature parameters were extracted from the curves of electronic nose sensors : stable value, area value, mean differential value, wavelet energy value and the coefficients of the fitted quadratic polynomial function. Feature parameters were selected based on three kinds of neural network methods: multilayer perceptron neural network (MLPNN), radial basis neural network (RBFNN) and extreme learning machine (ELM). Then stable value, area value and mean differential value were selected because of their better classification performance among the five kinds of feature parameters. Multiple-features were combinations of single-features. The classification analysis was carried out based on multiple-features and three kinds of neural network methods. And support vector machine regression (SVR) models were established based on single-features and multiple-features, respectively. The results showed that the classification performance of multiple-features was better than that of single-features. The classification performance was best based on "stable value and mean differential value" features and ELM. The classification accuracy of training set and test set based on "stable value and mean differential value" features were both 100%. The regression models based on multiple-features were better than that based on single-features. And the regression model was the best based on "area value and mean differential value" features. The coefficient of determination (R2) and root mean square error (RMSE) of the regression model based on the training set of "area value and mean differential value" were 0.994 0 and 0.086 0. The R2 and RMSE of the regression model based on the test set of "area value and mean differential value" were 0.923 0 and 0.370 9. The results show that feature election and multiple-features can improve the classification performance of the electronic nose for infested cotton plants. It can be concluded that electronic nose has strong potential for the application of detection of cotton plants infested with cotton bollworm at an early stage. |
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| Keywords: | electronic nose neural network prediction cotton plants cotton bollworm feature election |
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