Abstract: 【Aim】 In order to find a convenient way to recognize insect species for those worked in agriculture, forestry, plant quarantine etc., we developed a novel method to recognize images of lepidopteran insects. 【Methods】 Firstly, the background of captured specimen image is removed and then the wings are cut out and calibrated in the preprocessing period. Then the calibrated wing is segmented into a number of super pixels, and mean values of l, a and b in color space and x and y in Cartesian coordinate system are kept as feature data. Following that, the sparse coding (SC) algorithm is used to train the codebook, generate the sparse codes that are pooled into a feature vector to train the SCG (Scaled Conjugate Gradient) Back Propagation Neural Network (BPNN). Finally the resulting BPNN is used to classify and recognize unknown insects. 【Results】 The proposed method was tested in a database with 576 images with the best recognition rate over 99%, and the system also demonstrated ideal time performance, good robusticity and stability. 【Conclusion】 The experimental results proved the efficiency of the proposed method in recognizing images of lepidopteran insects.

Key words: Insect, Lepidoptera, image recognition, super pixel segmentation, sparse coding, Scaled Conjugate Gradient (SCG), Back Propagation Neural Network (BPNN)