title: Estimation of the age and amount of brown rice plant hoppers based on bionic electronic nose use.
abstract: 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.
authors.affiliation: Key Laboratory of Key Technology on Agricultural Machine and Equipment, South China Agricultural University, Guangzhou 510642, China. xusai1991@sina.cn.
authors.forename: Sai
authors.initials: S
authors.lastname: Xu
authors.affiliation: Key Laboratory of Key Technology on Agricultural Machine and Equipment, South China Agricultural University, Guangzhou 510642, China. zyzhou@scau.edu.cn.
authors.forename: Zhiyan
authors.initials: Z
authors.lastname: Zhou
authors.affiliation: Key Laboratory of Key Technology on Agricultural Machine and Equipment, South China Agricultural University, Guangzhou 510642, China. huazlu@scau.edu.cn.
authors.forename: Huazhong
authors.initials: H
authors.lastname: Lu
authors.affiliation: Key Laboratory of Key Technology on Agricultural Machine and Equipment, South China Agricultural University, Guangzhou 510642, China. xwluo@scau.edu.cn.
authors.forename: Xiwen
authors.initials: X
authors.lastname: Luo
authors.affiliation: Key Laboratory of Key Technology on Agricultural Machine and Equipment, South China Agricultural University, Guangzhou 510642, China. ylan@tamu.edu.
authors.forename: Yubin
authors.initials: Y
authors.lastname: Lan
authors.affiliation: Plant Protection Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China. zhangy@gdppri.com.
authors.forename: Yang
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Plant Protection Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China. liyf458@tom.com.
authors.forename: Yanfang
authors.initials: Y
authors.lastname: Li
mesh_terms: D000818:Animals; D001208:Asia; D001701:Bionics; D016002:Discriminant Analysis; D062609:Electronic Nose; D006430:Hemiptera / Q000472:pathogenicity / Q000502:physiology*; D007300:Insect Control; D016571:Neural Networks, Computer; D012275:Oryza / Q000469:parasitology; D025341:Principal Component Analysis; D012044:Regression Analysis