"Pest Facial Recognition" & Intelligent Warning: Core Advantages of Insect Monitoring Station

In agricultural production, pest infestations restrict crop yield and quality. Traditional manual inspection-based monitoring is labor-intensive and time-consuming, with limitations such as a narrow coverage range and delayed data, making it difficult to provide accurate support for pest prevention and control. With the advancement of agricultural modernization, the Insect Monitoring Station has become an "intelligent guardian" for pest early warning and prevention.

The system uses solar energy as its core power source and consists of a solar power supply module, a multi-functional pest processing module, a high-definition image acquisition module, a wireless data transmission module, and a smart agriculture terminal platform. Its solar power supply offers distinct advantages: during the day, it is powered by high-efficiency crystalline silicon solar panels while storing electricity; at night or in rainy weather, it switches to energy storage batteries for power supply. This ensures 24-hour operation without the need for an external power grid, allowing flexible deployment in remote areas and reducing installation and usage costs.

The core process of pest monitoring achieves automation in multiple links and integrates "pest facial recognition" technology. The multi-functional module traps pests using a pest monitoring lamp, followed by automatic pest killing and drying (to keep pest bodies clear); after a high-definition camera takes photos of the pest bodies, the data is uploaded to the platform in real time. The "pest facial recognition" technology completes pest species identification, verification, classification, and quantity statistics within seconds, generating data reports and trend charts.

To meet the "early detection and early prevention" demand for pest control, the system is equipped with an intelligent early warning function. When the number of pests exceeds the threshold, it automatically sends early warning messages to the staff's terminals. It enables accurate monitoring of major pests such as fall armyworms and rice leaf folders, improving the effectiveness of prevention and control.

Compared with traditional methods, this system realizes full-process automation and intelligence in pest monitoring, enhances the scientificity and timeliness of pest forecasting, reduces human errors and costs, and provides a scientific basis for predicting pest occurrences. It meets the requirements of modernized pest forecasting and contributes to green pest prevention in agriculture and high-quality agricultural development.