An edge computing model applied to the identification of invasive species in Yunnan Province

doi:10.14088/j.cnki.issn0439-8114.2023.10.036

Abstract

Abstract: Based on the MobileNet model migration, four main invasive species （Bidens bipinnata, Cassia plant, water peanut, and Eupatorium adenophorum） images in Yunnan Province were identified. The MobileNet-v2 models with width ratios of 1.0 and 1.4 were applied to the dataset in this study for experiments. The reasons for the unstable recognition of the MobileNet-v2 network model were analyzed, and the model was improved by adding channel attention mechanism modules, updating activation functions, and compressing network layers. The results showed that the improved MobileNet-v2 model had a recognition accuracy of 96.8%, and the model parameter quantity was only 15 359 093. The improved MobileNet-v2 model had high recognition accuracy and fewer model parameters, making it suitable for deployment at the edge, and could be better applied to the field of invasive species control in Yunnan Province.

Key words: invasive species, edge computing model, MobileNet-v2 model, Yunnan Province

CLC Number:

LUO Ling, SONG Ke, WANG Hao, ZI Cai-fei, FENG Wei, DU Ming-ming, SUN Zhong-xiang, CAO Zhi-yong. An edge computing model applied to the identification of invasive species in Yunnan Province[J]. HUBEI AGRICULTURAL SCIENCES, 2023, 62(10): 212-217.

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