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With the changes in the ecological environment and the increase in human activities, the identification and prevention of poisonous species have become an important issue in public safety. However, traditional identification methods rely on a large number of samples and expert experience, which is inefficient and costly. This paper proposes a popular science platform for poisonous species identification based on small sample learning, aiming to solve the problems of scarce poisonous species samples and low identification accuracy, help users quickly and accurately identify poisonous species, and instantly obtain relevant popular science knowledge and preventive measures. In terms of platform design, we adopted a modular idea and divided the system into a front-end display layer, a back-end service layer, and a data processing layer. In response to the problem of sample scarcity, we used small sample learning methods such as data enhancement technology, transfer learning technology, and metric learning to achieve high-precision identification. In addition, we also designed a popular science module, including basic knowledge introduction of poisonous species, identification skills, preventive measures, etc., to help users improve their awareness of prevention and master self-protection skills. In summary, the popular science platform for poisonous species identification based on small sample learning designed in this paper is an innovative solution, which provides new ideas and methods for solving the problem of poisonous species identification. In the future, we will continue to optimize platform functions, improve recognition accuracy and efficiency, and explore more application scenarios and promotion models to better serve the public and the cause of ecological and environmental protection.
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