Intelligent Pest Monitoring System: Pest and Disease Investigation Statistics and IoT Integration Solution

The Key Role of Pest and Disease Investigation in Modern Agricultural Engineering

In the agricultural production chain, pest and disease control directly affects crop yield and food safety. With the advancement of sustainable development policies, the zero-growth action in pesticide use requires technicians to master pest dynamics through scientific investigations to avoid blind application. Traditional manual recording methods are inefficient and easily constrained by field environments, while intelligent pest monitoring systems provide automated solutions to improve the accuracy and timeliness of investigation statistics.

NiuBoL's intelligent remote pest monitoring system integrates photoelectric trapping, far-infrared processing, and IoT technology to achieve unattended insect collection, drying, and image upload. The system supports seamless docking with meteorological and soil sensors to form a comprehensive monitoring platform. Project contractors can utilize its open architecture to deploy in distributed farm networks, realizing data-driven control strategies, reducing chemical interventions, and complying with environmental regulations.

Core Functions and Advantages of the Pest Monitoring System

The system adopts stainless steel and galvanized spray-coated structure to ensure outdoor corrosion resistance for more than 2 years. Through industrial cameras and AI recognition technology, it automatically collects 12-megapixel pest images and uploads them to the cloud platform for species and quantity analysis. This not only simplifies the pest and disease investigation process but also provides structured data support for subsequent statistics.

At the integration level, the system reserves IoT link ports, supporting Modbus protocol and API calls, making it easy for IoT solution providers to embed into existing SCADA systems. For example, engineering companies can fuse pest data with GIS platforms to generate real-time pest distribution maps, guiding regional control measures. This data-oriented approach significantly improves project efficiency and reduces labor input.

Technical Parameters and Engineering Specifications of the Pest Monitoring System

NiuBoL's intelligent remote pest monitoring system design focuses on engineering reliability. The following table outlines key parameters for specification selection and project tendering:

These parameters ensure stable operation of the system under IP65 protection rating, suitable for harsh outdoor environments.

Pest Monitoring System Integration Solution: Compatibility and Expansion Framework

NiuBoL pest monitoring system emphasizes modular design, supporting multiple communication protocols including 485/232 and 4G/Ethernet, facilitating system integrators in developing customized solutions. Through the Android tablet interface, users can set time control modes and define work cycles based on target pest habits for refined management.

In terms of compatibility, the system can integrate with third-party sensors, such as connecting soil moisture or weather station data to form a multi-source fusion platform. IoT solution providers can use API interfaces to import pest data into big data analysis engines for predictive modeling. For example, combined with machine learning algorithms, the system can provide early warnings of pest outbreaks and optimize pesticide spraying schedules.

The expansion framework supports remote wireless restart and debugging, reducing on-site maintenance needs. When deploying large networks, project contractors can adopt solar power mode to lower infrastructure costs and monitor equipment status through cloud platforms to ensure system redundancy and high availability. This integration approach not only enhances the scalability of engineering projects but also supports data export to ERP systems for compliance auditing and performance evaluation.

Detailed Explanation of the Working Principle of the Pest Monitoring System

The system's operation is based on multiple control logics to ensure efficient performance:

• Light Control Principle: Ambient light sensor detects day-night changes; disconnects circuit to enter standby during the day and closes to start the trapping lamp at night.

• Time Control Principle: Supports 4 custom work periods, with data uploaded to the server for easy adjustment based on pest activity patterns.

• Rain Control Principle: Rain sensor triggers drainage system to prevent rainwater from entering insect channels; automatically resumes after rain stops.

• Insect Processing Principle: LED tubes attract pests; after hitting the impact screen, they fall into far-infrared chamber (death in 3-5 minutes), then baked dry (90℃, 15 minutes), vibrated to flatten, photographed, and insect collection tray cleaned.

This principle design optimizes the insect processing flow and supports trapping of 1326 pest species, covering vegetables, rice, cotton, and other categories for comprehensive coverage.

Project Application Cases of the Pest Monitoring System

The NiuBoL system has verified its effectiveness in multiple engineering projects. For example, in a 2000-acre rice planting project, an IoT solution provider integrated 30 pest monitoring lamps and connected them to a central platform via 4G network. The system uploads AI recognition data in real time, combined with meteorological inputs to predict rice planthopper outbreaks and guide precise control. Results showed a 20% reduction in pesticide usage, 15% decrease in crop losses, and significant improvement in project ROI.

Expanded Application Scenarios of the Pest Monitoring System

NiuBoL intelligent pest monitoring system is suitable for diverse engineering scenarios, assisting partners in delivering efficient solutions:

• Large Farm Networks: Deploy distributed nodes in grain crop areas, supporting precision agriculture platform integration, real-time monitoring of underground pests such as cutworms, achieving pesticide reduction.

• Forestry and Grassland Protection: For forest pests such as American white moths, combined with terrain data to provide regional early warnings, suitable for ecological restoration projects.

• Vegetable and Orchard Management: In greenhouses or fruit orchards, dock with irrigation systems to optimize time control and reduce losses from borers.

• Tobacco and Tea Planting: AI recognition supports classification of specific pests such as tobacco budworms, integrated into supply chain management systems to improve product quality control.

• Urban Greening and Landscaping Engineering: Solar-powered, low-power design suitable for city parks, with lightning protection devices ensuring safety.

• Quarantine and Customs Applications: Image upload and drying functions provide reliable samples for import/export plant quarantine, supporting international standard compliance.

• Scientific Research and Education Projects: Open interfaces facilitate data access by institutions for pest prediction model development and teaching demonstrations.

• Storage and Livestock Pest Control: For storage pests such as grain moths, supports mixed indoor-outdoor deployment, integrating environmental sensors to maintain biological chain balance.

These scenarios highlight the system's multifunctionality and support engineering companies in expanding service scope.

FAQ

Q1. How does the NiuBoL pest monitoring system support multi-sensor integration?
The system reserves 485/232 ports and IoT links, supports Modbus protocol, can connect to meteorological and soil sensors, and achieves comprehensive analysis through cloud platform data fusion.

Q2. What is the specific accuracy rate of the AI insect identification function?
Verified by CMA/CNAS testing, the recognition accuracy exceeds 90%, can automatically classify 1326 pest species including types, names, and quantities, and supports custom training to improve precision.

Q3. How stable is the system in rainy environments?
Rain control sensor automatically triggers drainage, insect-water separation mechanism prevents water accumulation; louver design isolates rainwater, ensuring continuous operation under IP65 protection rating.

Q4. What options are included in the remote management functions?
Supports PC-side wireless restart, remote debugging, and location mapping, providing long-distance technical support, suitable for distributed project monitoring.

Q5. How is the endurance capability in solar power mode?
400W solar panel and 200AH battery can maintain operation for 3-5 days during cloudy/rainy periods, with high conversion efficiency, suitable for remote engineering sites.

Q6. How is data upload and analysis achieved?
Real-time upload of images and status to IoT platform via 4G/Ethernet, supports export to databases for big data analysis and report generation.

Q7. How customizable is the pest processing flow?
Drying time and temperature can be set, time control supports 4 periods, adjustable according to target pest habits to ensure processing efficiency and insect body integrity.

Summary: A Reliable Plant Protection Engineering Partner

NiuBoL's intelligent remote pest monitoring system provides a comprehensive pest and disease investigation statistics solution. Through advanced integration and compatibility, it assists system integrators and project contractors in achieving zero-growth pesticide use goals. The system not only optimizes data collection and analysis processes but has also proven its reliability and efficiency in actual engineering. Choosing NiuBoL means investing in the future of sustainable agriculture and building an efficient, green plant protection ecosystem.

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Intelligent Remote Insect Monitoring and Reporting System(Insect Monitoring Device) Data Sheet

Intelligent Remote Insect Monitoring and Reporting System(Insect Monitoring Device) Data Sheet.pdf