Detection of stored-grain insects using deep learning

A detection and identification method for stored-grain insects was developed by applying deep neural network. Adults of following six species of common stored-grain insects mixed with grain and dockage were artificially added into the developed insect-trapping device: Cryptoleste Pusillus(S.), Sitophilus Oryzae(L.), Oryzaephilus Surinamensis(L.), Tribolium Confusum(Jaquelin Du Val), Rhizopertha Dominica(F.). Database of Red Green and Blue (RGB) images of these live insects was established. We used Faster R-CNN to extract areas which might contain the insects in these images and classify the insects in these areas. An improved inception network was developed to extract feature maps. Excellent results for the detection and classification of these insects were achieved. The test results showed that the developed method could detect and identify insects under stored grain condition, and its mean Average Precision (mAP) reached 88.