Fuzzy classification of orchard pest posture based on Zernike moments

Identification and count of orchard pests is very important in monitoring orchard pest population. The pests trapped by high-voltage grid show different postures and incomplete bodies, which increase the difficulty of image automated identification. Currently, most researches of pest image identification focus on feature extraction based on standard posture samples, without considering the influence from multi-pose of pests in natural scene. Consequently, the identification rates of these methods are low in practical orchard application. Using Dichocrocis punctiferalis (Guenee) as research object, this paper is directed towards a posture classification method for the orchard target pest identification. It aims at intensifying the performance of multi-pose pest identification system by utilizing Zernike moments as descriptors of shape characteristics. The input image is cropped automatically and further subjected to a number of preprocessing stages. The outcome of preprocessing stage is one processed image containing scaled and translated target pest. Then, the template number is determined according to the posture of target pest and the corresponding template parameters are obtained from the cluster centers by fuzzy C-mean clustering method. Experiment results show that the proposed shape feature is robust to changes caused by pest image shape rotation, translation, and/or scaling. And the highest accuracy of posture classification is 92.3% for orchard target pest Dichocrocis punctiferalis (Guenee) with multiple postures. It outperforms the method in reference [2] where the highest accuracy is 86.6%.