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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The cotton pests have the characteristics of concealment, migration, and sudden burst, and there are many influencing factors involved. The accurate diagnosis of cotton pests is a difficult problem in the agricultural field. Previous studies have demonstrated that cotton plants produce blends of volatile compounds in response to herbivores serve as cues for parasitic and predatory insects. Therefore, it is possible to obtain information about cotton pests by detecting volatile compounds in cotton. In this study, an electronic nose was used to detect the volatiles emitted by cotton plants damaged by cotton bollworm at the flowering period. The cotton samples were divided into four infested cotton treatments. According to the number of pests in each pot of cotton seedlings, the treatments inoculated with 0, 1, 2, and 3 bollworm larvae were marked as 0-P, 1-P, 2-P, and 3-P, respectively. The 0-P was healthy cotton as a control treatment. The cotton bollworm feeding lasted 48 h. During this period, the electronic nose detection tests were performed every 6 h, and a total of 8 repeated tests were performed. Appropriate pattern recognition techniques were applied to construct reliable algorithms for interpreting the acquired signal in cotton. Principal Component Analysis (PCA), discriminant function analysis, cluster analysis, and Radial Basis Function Neural Network (RBFNN) were applied to evaluate the data. The results of PCA and discrimination values of the healthy cotton treatment showed that the volatiles released by healthy cotton had obvious circadian rhythm. For the three infested cotton treatments, whereas the distribution patterns of cotton samples were different from that of the healthy cotton treatment. The three infested cotton treatments had regular distribution trends that cotton samples changed along the direction of the first and second principal components. Cluster analysis results showed that the four cotton treatments were all finally divided into two categories, the healthy cotton treatment, and the three infested cotton treatments. All these results suggested that there was a significant difference between healthy and damaged cotton samples. Then RBFNN was used to analyze four treatments of cotton samples at 8 different times. The results showed that the total correct rate of the test sets was 73.4%, the correct rate of the healthy cotton treatment was 100%, and the misjudgment samples appeared among the three infested cotton treatments. Moreover, two unified consecutive prediction models were established regardless of the time factor. The RBFNN model was established by using four treatments of cotton samples. The correct rate of the training sets was 66.1%, and the correct rates of the test sets were 100 %, 79.7 %, 32.8 %, and 20.3 % for the 0-P, 1-P, 2-P, and 3-P treatments, respectively. In another RBFNN model based on 0-P, 1-P, and 3-P treatments, the correct rate of the training sets was 87.8%, and the correct rates of the test sets were 100 %, 78.1%, and 82.8% for the 0-P, 1-P, and 3-P treatments, respectively. Comparing the results of the two RBFNN models, the prediction accuracy of the second model had been greatly improved. At the same time, it was also found that the prediction accuracy of all RBFNN models for healthy cotton treatment reached 100%. Therefore, the electronic nose could be used as an effective monitoring method for the occurrence of cotton bollworm in the cotton plants. It should have a potential application for crop pest monitoring in the field. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]