Identification of species and geographical strains of Sitophilus oryzae and Sitophilus zeamais using VIS / NIR hyperspectral imaging technique

Identifying stored-product insects is essential for granary management. Automated, computerbased 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 analyze the resultant hyperspectral data. SULTS: 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 back propagation 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. This study demonstrated that hyperspectral imaging, together with the appropriate recognition method, could provide a means for identifying insects and become a useful tool for identification of Sitophilus oryzae and Sitophilus zeamais to aid in the management of stored-product insects.

[1]  R. ffrench-Constant,et al.  Molecular and Morphological Characters Discriminate Sitophilus oryzae and S. zeamais (Coleoptera: Curculionidae) and Confirm Reproductive Isolation , 1996 .

[2]  John Chambers,et al.  Detection of external and internal insect infestation in wheat by near-infrared reflectance spectroscopy , 1996 .

[3]  P. W. Flinn,et al.  DETECTION OF INSECTS IN BULK WHEAT SAMPLES WITH MACHINE VISION , 1998 .

[4]  J. Baker,et al.  Automated Nondestructive Detection of Internal Insect Infestation of Wheat Kernels by Using Near-Infrared Reflectance Spectroscopy , 1998 .

[5]  Floyd E. Dowell,et al.  Identifying stored-grain insects using near-infrared spectroscopy. , 1999 .

[6]  John Chambers,et al.  Detection of Grain Weevils inside Single Wheat Kernels by a Very near Infrared Two-Wavelength Model , 1999 .

[7]  Floyd E. Dowell,et al.  Detection of Parasitized Rice Weevils in Wheat Kernels with Near-Infrared Spectroscopy1☆ , 1999 .

[8]  Huei-Jeng Lin,et al.  DNA identification of two laboratory colonies of the weevils, Sitophilus oryzae (L.) and S. zeamais Motschulsky (Coleoptera: Curculionidae) in Taiwan , 2003 .

[9]  Floyd E. Dowell,et al.  AUTOMATED DETECTION OF SINGLE WHEAT KERNELS CONTAINING LIVE OR DEAD INSECTS USING NEAR–INFRARED REFLECTANCE SPECTROSCOPY , 2003 .

[10]  Floyd E. Dowell,et al.  Detection of insect fragments in wheat flour by near-infrared spectroscopy $ , 2003 .

[11]  Junhong Liu,et al.  Single-Kernel Maize Analysis by Near-Infrared Hyperspectral Imaging , 2004 .

[12]  Yves Roggo,et al.  Infrared hyperspectral imaging for qualitative analysis of pharmaceutical solid forms , 2005 .

[13]  Noel D.G. White,et al.  Detection techniques for stored-product insects in grain , 2007 .

[14]  Saleh M. Ashaghathra.  Identification of Pecan Weevils through image processing , 2008 .

[15]  Jiewen Zhao,et al.  Automated tea quality classification by hyperspectral imaging. , 2009, Applied optics.

[16]  Chenglu Wen,et al.  Local feature-based identification and classification for orchard insects , 2009 .

[17]  Noel D.G. White,et al.  Detection of insect-damaged wheat kernels using near-infrared hyperspectral imaging , 2009 .

[18]  Wolfram Mauser,et al.  Inversion of a canopy reflectance model using hyperspectral imagery for monitoring wheat growth and estimating yield , 2009, Precision Agriculture.

[19]  Thomas G. Dietterich,et al.  Haar Random Forest Features and SVM Spatial Matching Kernel for Stonefly Species Identification , 2010, 2010 20th International Conference on Pattern Recognition.

[20]  Noel D.G. White,et al.  Identification of insect-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging , 2010 .

[21]  Gamal ElMasry,et al.  Quality classification of cooked, sliced turkey hams using NIR hyperspectral imaging system , 2011 .

[22]  Shiv O. Prasher,et al.  ARTIFICIAL NEURAL NETWORK MODELING OF HYPERSPECTRAL RADIOMETRIC DATA FOR QUALITY CHANGES ASSOCIATED WITH AVOCADOS DURING STORAGE , 2011 .

[23]  R. Cruickshank,et al.  Molecular phylogenetics of a South Pacific sap beetle species complex (Carpophilus spp., Coleoptera: Nitidulidae). , 2012, Molecular phylogenetics and evolution.

[24]  Shu-Chen Chang,et al.  A SCAR-Based Method for Rapid Identification of Four Major Lepidopterous Stored-Product Pests , 2012, Journal of economic entomology.

[25]  P. Ebert,et al.  Phosphine Resistance in the Rust Red Flour Beetle, Tribolium castaneum (Coleoptera: Tenebrionidae): Inheritance, Gene Interactions and Fitness Costs , 2012, PloS one.

[26]  S. Nagalakshmi,et al.  On-line evaluation of loadability limit for pool model with TCSC using back propagation neural network , 2013 .

[27]  Jun Wang,et al.  Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage and Chinese vinegar , 2013 .

[28]  Yu-De Lin,et al.  Automatic defect inspection system of colour filters using Taguchi-based neural network , 2013 .

[29]  Mark Beale,et al.  Neural Network Toolbox™ User's Guide , 2015 .