Estimation of the Age and Amount of Brown Rice Plant Hoppers Based on Bionic Electronic Nose Use

The brown rice plant hopper (BRPH), Nilaparvata lugens (Stal), is one of the most important insect pests affecting rice and causes serious damage to the yield and quality of rice plants in Asia. This study used bionic electronic nose technology to sample BRPH volatiles, which vary in age and amount. Principal component analysis (PCA), linear discrimination analysis (LDA), probabilistic neural network (PNN), BP neural network (BPNN) and loading analysis (Loadings) techniques were used to analyze the sampling data. The results indicate that the PCA and LDA classification ability is poor, but the LDA classification displays superior performance relative to PCA. When a PNN was used to evaluate the BRPH age and amount, the classification rates of the training set were 100% and 96.67%, respectively, and the classification rates of the test set were 90.67% and 64.67%, respectively. When BPNN was used for the evaluation of the BRPH age and amount, the classification accuracies of the training set were 100% and 48.93%, respectively, and the classification accuracies of the test set were 96.67% and 47.33%, respectively. Loadings for BRPH volatiles indicate that the main elements of BRPHs' volatiles are sulfur-containing organics, aromatics, sulfur- and chlorine-containing organics and nitrogen oxides, which provide a reference for sensors chosen when exploited in specialized BRPH identification devices. This research proves the feasibility and broad application prospects of bionic electronic noses for BRPH recognition.

[1]  Giorgio Sberveglieri,et al.  Detection of toxigenic strains of Fusarium verticillioides in corn by electronic olfactory system , 2005 .

[2]  Zheng Limin Electronic nose and the development of its application to classify of dairy products , 2006 .

[3]  Wu GuoRui,et al.  Long-term forecast on the outbreak of brown planthopper (Nilaparvata lugens Stȧl) and white-backed planthopper (Sogatella furcifera Horvath). , 1997 .

[4]  Li San-ping Realization of Handwritten Numeral Recognition System Based on PNN with MATLAB , 2005 .

[5]  John C. Morgan,et al.  Geographical variation in acoustic signals of the planthopper, Nilaparvata bakeri (Muir), in Asia: species recognition and sexual selection , 1993 .

[6]  Tu Kang,et al.  Determination of egg freshness during shelf life with electronic nose. , 2010 .

[7]  J. Riley,et al.  The long‐distance migration of Nilaparvata lugens (Stål) (Delphacidae) in China: radar observations of mass return flight in the autumn , 1991 .

[8]  R. K. Butlin The variability of mating signals and preferences in the brown planthopper,Nilaparvata lugens (Homoptera: Delphacidae) , 2005, Journal of Insect Behavior.

[9]  C. Natale,et al.  An electronic nose and a mass spectrometry-based electronic nose for assessing apple quality during shelf life , 2004 .

[10]  M. Bharathi,et al.  Expression of snowdrop lectin (GNA) in transgenic rice plants confers resistance to rice brown planthopper. , 1998, The Plant journal : for cell and molecular biology.

[11]  Yi Hua-hui Equipment Fault Diagnosis based on Probabilistic Neural Networks and Simulation Analysis , 2009 .

[12]  N. Magan,et al.  Electronic noses and disease diagnostics , 2004, Nature Reviews Microbiology.

[13]  Junfeng Jing,et al.  Automatic classification of woven fabric structure based on texture feature and PNN , 2014, Fibers and Polymers.

[14]  M Hajmeer,et al.  A probabilistic neural network approach for modeling and classification of bacterial growth/no-growth data. , 2002, Journal of microbiological methods.

[15]  M. Cohen,et al.  Detection and analysis of QTLs for resistance to the brown planthopper, Nilaparvata lugens, in a doubled-haploid rice population , 1998, Theoretical and Applied Genetics.

[16]  J Du,et al.  ESTIMATION OF TOTAL AMOUNT OF SAP INGESTED BY NILAPARVATA LUGENS STAL UNDER EXPERIMENTAL CONDITION , 1991 .

[17]  Shattri Mansor,et al.  Automated Identification And Counting Of Pests In The Paddy Fields Using Image Analysis , 2006 .

[18]  Bo Zhou,et al.  Discrimination of different types damage of rice plants by electronic nose , 2011 .

[19]  Jie Hu,et al.  Application of PCA Method on Pest Information Detection of Electronic Nose , 2006, 2006 IEEE International Conference on Information Acquisition.

[20]  Rong-Kuen Chen,et al.  Changes in spectral characteristics of rice canopy infested with brown planthopper and leaffolder , 2007 .

[21]  Dong Hyawn Kim,et al.  Application of probabilistic neural network to design breakwater armor blocks , 2008 .

[22]  Bo Zhong,et al.  BP neural network with rough set for short term load forecasting , 2009, Expert Syst. Appl..

[23]  Anne-Claude Romain,et al.  The use of sensor arrays for environmental monitoring: interests and limitations. , 2003, Journal of environmental monitoring : JEM.

[24]  Annia García Pereira,et al.  Discrimination of storage shelf-life for mandarin by electronic nose technique , 2007 .

[25]  Jun Wang,et al.  Use of electronic nose technology for identifying rice infestation by Nilaparvata lugens , 2011 .

[26]  Jie Hu,et al.  Insect herbivory information detection by Principal Component Analysis on Electronic Nose System , 2005, 2005 International Conference on Neural Networks and Brain.

[27]  Shekar Viswanathan,et al.  Detection and Analysis of Volatile Organic Chemicals in Waste Water using an Electronic Nose , 2006 .

[28]  José Antonio Lozano,et al.  Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Liao Xi-yuan,et al.  Food Safety and Rice Production in China , 2005 .

[30]  Jia Zong-yan Electronic tongue and its application in beverage recognition , 2006 .

[31]  Mikhail P. Moshkin,et al.  Detection of Helicobacter pylori infection by examination of human breath odor using electronic nose Bloodhound‐214ST , 2009 .

[32]  Yang Pei-qiang Fast Detection of Fried Oil Quality by Electronic Nose , 2013 .