title: Identification of species and geographical strains of Sitophilus oryzae and Sitophilus zeamais using the visible/near-infrared hyperspectral imaging technique.
abstract: BACKGROUND
  Identifying stored-product insects is essential for granary management. Automated, computer-based classification methods are rapidly developing in many areas. A hyperspectral imaging technique could potentially be developed to identify stored-product insect species and geographical strains. This study tested and adapted the technique using four geographical strains of each of two insect species, the rice weevil and maize weevil, to collect and analyse the resultant hyperspectral data.
  
  
  RESULTS
  Three characteristic images that corresponded to the dominant wavelengths, 505, 659 and 955 nm, were selected by multivariate image analysis. Each image was processed, and 22 morphological and textural features from regions of interest were extracted as the inputs for an identification model. We found the backpropagation neural network model to be the superior method for distinguishing between the insect species and geographical strains. The overall recognition rates of the classification model for insect species were 100 and 98.13% for the calibration and prediction sets respectively, while the rates of the model for geographical strains were 94.17 and 86.88% respectively.
  
  
  CONCLUSIONS
  This study has demonstrated that hyperspectral imaging, together with the appropriate recognition method, could provide a potential instrument for identifying insects and could become a useful tool for identification of Sitophilus oryzae and Sitophilus zeamais to aid in the management of stored-product insects.
authors.affiliation: Academy of State Administration of Grain, Beijing, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Cao
authors.affiliation: School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China.
authors.forename: Chaojie
authors.initials: C
authors.lastname: Zhang
authors.affiliation: School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China.
authors.forename: Quansheng
authors.initials: Q
authors.lastname: Chen
authors.affiliation: Academy of State Administration of Grain, Beijing, China.
authors.forename: Yanyu
authors.initials: Y
authors.lastname: Li
authors.affiliation: School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China.
authors.forename: Shuai
authors.initials: S
authors.lastname: Qi
authors.affiliation: Academy of State Administration of Grain, Beijing, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Tian
authors.affiliation: Murdoch University, Murdoch, WA, Australia.
authors.forename: YongLin
authors.initials: Y
authors.lastname: Ren
mesh_terms: D000818:Animals; D002138:Calibration; D002681:China; D005260:Female; D008297:Male; D061848:Optical Imaging / Q000379:methods*; D019265:Spectroscopy, Near-Infrared; D048889:Weevils / Q000145:classification*
keywords: geographical strains; hyperspectral imaging; identification; maize weevil; rice weevil; stored-product insects