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

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 analyze 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 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 article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/ps.3893