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As vegetable safety is a rather important issue related to people's health and life, it is fundamental to ensure the vegetable safety by supervising the whole procedure of vegetable production. This requires to control the use of pesticides via accurate pesticide spraying according to the pest situation, which is the best strategy for vegetable safety. The key issue achieving this objective is to find out the species, quantity, and distribution of pests and the harm degree of vegetables. Although the pest identification via image processing has been widely used in recent years, it merely handles small pests of vegetables in the laboratory, and the number of pest species simultaneously processed is also limited to 1 or 2. To better recognize pests, this paper proposes a new algorithm to identify a number of vegetable pests such as striped flea beetle, whiteflies, diamondback moth, and thrips by deploying the support vector machine (SVM) and the region growing algorithm. This scheme integrates the recognition process into the segmentation one and uses the grid method to select seed points for region growing, which in turn simplifies stages of image processing. In performance assessment, 100 samples are adopted for each test pest, among which 60 are for training and the others for testing. Experimental results show that the proposed scheme can correctly identify the aforementioned major 4 vegetable pests in south China with a recognition rate of more than 93%. This implies that the proposed scheme achieves classification of several pests and thus would be promising in practical applications. [ABSTRACT FROM AUTHOR]