I. Device Introduction

The ZD-GS-A33 Small Insect Monitoring and Early Warning System is a pest forecasting lamp specifically designed for trapping and monitoring micro-insects such as rice planthoppers and leafhoppers. Utilizing modern optical, electronic, and digital control technologies, this device automates processes including insect attraction, extermination, photography, insect removal, collection, and drainage. It can upload environmental conditions and pest status in real-time to designated network servers, facilitating backend identification and analysis of uploaded images. The system overcomes key technical challenges such as live insect imaging, specialized insect-attracting spectra, and deep learning algorithm recognition, filling the current gap in millimeter-level insect forecasting. Widely used in agriculture, forestry, and other fields, it provides effective technical support for pest control.

Common Pest Monitoring Applications:

Wheat fields: Aphids, wheat thrips, etc.;

Vegetable gardens: Whiteflies, vegetable thrips, etc.;

Rice fields: Whiteflies, rice planthoppers, rice leafhoppers, etc.

II. Technical Parameters:

1. Insect-attracting light source: Default multi-wavelength insect-attracting light source with enhanced specificity (supports custom wavelength insect-attracting tubes).

2. Exterior structure: Constructed with stainless steel electrostatic powder coating, robust and durable, offering high cost-effectiveness.

3. Extermination method: Uses multi-spectral insect-attracting light to lure insects, with a high-speed fan generating negative air pressure to suck insects into the collection box for air-drying extermination. More environmentally friendly and safe.

4. Insect entry port: Custom-designed entry port significantly increases the probability of pest entry.

5. Millimeter-level micro-insect identification: Specializes in trapping and monitoring micro-insects such as rice planthoppers and leafhoppers.

6. Insect filtration device: A filter screen around the capture port prevents non-target larger insects from entering the machine, ensuring accurate automatic identification of small insects.

7. Intelligent identification and automatic counting: The small insect intelligent forecasting system uses AI to automatically identify and count micro-insects such as brown planthoppers, white-backed planthoppers, and leafhoppers, with an identification accuracy rate of ≥95%.

8. Remote control and automatic photography: Supports viewing captured images via web and mobile apps, and allows remote configuration of photography and working modes through the platform.

9. High completeness of insect imaging: Equipped with a built-in 12-megapixel high-definition industrial camera for insect photography, achieving over 98% insect completeness.

10. Automatic cleaning: The system automatically cleans target insects after photography to avoid affecting subsequent imaging.

11. Built-in GPS positioning: Enables viewing of device site data on web maps.

12. Light control: Automatically turns on at night and off during the day (standby mode). During nighttime operation, it remains unaffected by sudden strong light exposure.

13. Rainproof design: Operates normally and continues trapping insects during rainy weather.

14. Supports meteorological configuration: Optional sensors for wind speed, wind direction, air temperature and humidity, atmospheric pressure, light intensity, and soil temperature and humidity enable monitoring of environmental meteorological data, providing data support for analyzing the relationship between pest occurrence and weather conditions.

15. Remote updates: Android system apps can be remotely updated via the cloud platform to upgrade functionalities and enhance user experience.

16. Time period control: Allows remote setting of time and parameters to define working periods based on the life habits of target pests.

17. Powerful data storage function: In case of network failure, the local data caching mechanism is activated, with a storage capacity of approximately 500,000 records. Cached data is automatically uploaded to the server once the network is restored, ensuring data security and preventing loss.