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Fine-Grained Pests Recognition Based on Truncated Probability Fusion Network via Internet of Things in Forestry and Agricultural Scenes

文献类型： 外文期刊

作者： Ma, Kai 1 ; Nie, Ming-Jun 2 ; Lin, Sen 3 ; Kong, Jianlei 4 ; Yang, Cheng-Cai 4 ; Liu, Jinhao 1 ;

作者机构： 1.Beijing Forestry Univ, Coll Engn, Beijing 100086, Peoples R China

2.Business Growth Business Unit, Beijing 100176, Peoples R China

3.Beijing Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

4.Beijing Technol & Business Univ, Sch Artificial Intelligence, Beijing 100048, Peoples R China

关键词： insect pest recognition; fine-grained visual classification; deep-learning neural network; soft-VLAD aggregation; gated probability fusion

期刊名称：ALGORITHMS （ 影响因子：2.3； 五年影响因子：2.2 ）

ISSN：

年卷期： 2021 年 14 卷 10 期

页码：

收录情况： SCI

摘要： Accurate identification of insect pests is the key to improve crop yield and ensure quality and safety. However, under the influence of environmental conditions, the same kind of pests show obvious differences in intraclass representation, while the different kinds of pests show slight similarities. The traditional methods have been difficult to deal with fine-grained identification of pests, and their practical deployment is low. In order to solve this problem, this paper uses a variety of equipment terminals in the agricultural Internet of Things to obtain a large number of pest images and proposes a fine-grained identification model of pests based on probability fusion network FPNT. This model designs a fine-grained feature extractor based on an optimized CSPNet backbone network, mining different levels of local feature expression that can distinguish subtle differences. After the integration of the NetVLAD aggregation layer, the gated probability fusion layer gives full play to the advantages of information complementarity and confidence coupling of multi-model fusion. The comparison test shows that the PFNT model has an average recognition accuracy of 93.18% for all kinds of pests, and its performance is better than other deep-learning methods, with the average processing time drop to 61 ms, which can meet the needs of fine-grained image recognition of pests in the Internet of Things in agricultural and forestry practice, and provide technical application reference for intelligent early warning and prevention of pests.

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