Citrus Pest Identification Model Based on Improved ShuffleNet
Source abstract
Abstract
To address the current issues of complex structures and low accuracies in citrus pest identification models, a lightweight pest identification model was proposed. First, a parameterized linear rectification function was introduced to avoid neuronal death. Second, the model’s attention to pest characteristics was improved by incorporating an improved mixed attention mechanism. Subsequently, the network structure of the original model was adjusted to reduce architectural complexity. Finally, by employing transfer learning, an SCHNet model was developed. The experimental results indicated that the proposed model achieved an accuracy rate of 94.48% with a compact size of 3.84 MB. Compared to the original ShuffleNet V2 network, the SCHNet model showed a 3.12% accuracy improvement while reducing the model size by 22.7%. The SCHNet model exhibited an excellent classification performance for citrus pest identification, enabling the accurate identification of citrus pests.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Agriculture and AI · Date Palm Research Studies · Phytoplasmas and Hemiptera pathogens
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Key Laboratory of Advanced Manufacturing and Automation Technology, Guangxi University, Guilin 541006, China
School of Mechanical and Control Engineering, Guilin University of Technology, Guilin 541006, China
School of Surveying, Mapping and Geographic Information, Guilin University of Technology, Guilin 541006, China
College of Marine Engineering, Jimei University, Xiamen 361021, China
Funding
National Natural Science Foundation of China · Guilin Technology Application and Promotion Plan · China-ASEAN Huawei Artificial Intelligence Innovation Center 2022 subsidy project
References (31)
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