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Detection and Identification of Stored Grain Insects with RF/Microwave and Neural Network Technology
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.orgCitation: 2008 Providence, Rhode Island, June 29 – July 2, 2008 082343.
Authors: Fujian Ding, Carol L Jones, Paul Weckler
Keywords: Radio frequency and microwave, identification and classification, detection, neural network, sensing, stored grain insects
Eight different kinds of stored grain insects were investigated in the radio frequency (RF) and microwave frequency range of 0.3 MHz—1200 MHz with a free space device using a vector network analyzer. Detection and identification analysis were performed using neural network techniques. One-, two-, and three-waveband sets were optimized in the frequency range to maximize the detection and identification recognition rate. The total recognition rate of identification for eight kinds of stored grain insects was 73% while the recognition rate of the detection of the empty device was 76% using the frequencies of 180.3, 1020, and 1032 MHz.