Abstract
Helicoverpa armigera (H.armigera) and Helicoverpa assulta (H.assulta) are the world-wide insects which mainly do harm to crops like cotton and tobacco, etc. The accurate gender identification of the insects is of great significance for the prediction of regional ratio and population quantity. The color images of the male and female adults of the two pieces of insects were acquired by CCD equipment, respectively. The image segmentation and the morphological methods were applied to remove tentacles and feet of insects. Thirty-six digital features of the insects were extracted, such as color, texture and invariant moment. The simulated annealing algorithm (SAA) extracted the partial features to compose of the optimal feature space by the fitness function. The 15 features were determined and the max fitness was 83.87%. The artificial bee colony (ABC) algorithm was used to optimize the penalty factor c and the kernel function parameter g of support vector machine (SVM). The recognition accuracy of the classification model reached 95.83% when c = 7.3454, g = 0.4436, which indicates that the gender identification of the two pieces of insects is feasible based on SAA-ABC-SVM technology.
Supported by the National Natural Science Foundation of China (Grant No. 31671580), the Key Technologies R&D Program of Henan Province, China (Grant No. 162102110112), and the Backdrop of Young Teachers Program, Universities of Henan Province, China (Grant No. 2011GGJS-094).
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Zhang, H., Zhu, Y., Tan, L., Liu, J. (2020). The Recognition of Adult Insects of Helicoverpa armigera and Helicoverpa assulta Based on SAA-ABC-SVM Technology. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1160. Springer, Singapore. https://doi.org/10.1007/978-981-15-3415-7_36
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