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The First Accelerator of the Optoelectronic Industry Park in Weifang High tech Zone, Shandong Province
Shandong Wanxiang Environmental Technology Co., Ltd
Intelligent insect detection and recognition system: AI empowers recognition to improve the accuracy of insect detection and reporting
Date: 2025-12-31Read: 0
The core requirement for insect pest monitoring and reporting is "accurate identification and rapid analysis". Traditional insect pest monitoring and reporting rely on manual identification of pest species and quantities, which has problems such as low efficiency, large errors, and high professional requirements, making it difficult to meet the precise prevention and control needs of large-scale agricultural production. The intelligent insect detection and recognition system is based on AI intelligent recognition technology, integrating fully automatic insect attraction, high-definition image acquisition, and IoT transmission technology to achieve the full process intelligence of "attraction photography recognition analysis warning", greatly improving the accuracy and efficiency of insect detection and reporting, and providing core technical support for precise prevention and control of modern agricultural pests and diseases.
Fully automatic insect attraction ensures accurate sample acquisition. The core of the system is equipped with intelligent insect detection and reporting equipment, which accurately attracts target pests through light controlled insect traps and guides them into infrared insecticidal chambers through impact screens. Far infrared insecticidal technology can accurately control the temperature (90 ± 5 ℃) and processing time inside the warehouse, ensuring that the mortality rate of pests is not less than 98%, while maximizing the preservation of insect integrity (not less than 95%), providing high-quality samples for AI recognition. The conveyor belt automatic insect feeding system can evenly spread and accurately deliver the insect bodies to the shooting area. Through vibration dispersion processing, it avoids overlapping and obstruction of the insect bodies, ensuring that the morphological characteristics of each insect body can be clearly captured. The 8-bit automatic transfer bin insect receiver can set working modes in different time periods, accurately matching monitoring time periods according to the living habits of different pests, ensuring that insect samples from different time periods are not confused, and providing accurate data for subsequent analysis of pest activity patterns in different time periods.
AI intelligent recognition core, breaking through the bottleneck of manual recognition. The system is equipped with a self training AI recognition algorithm, relying on a huge self owned insect database, which can automatically analyze the insect body images collected by high-definition cameras, quickly identify the types and quantities of pests, with high recognition accuracy and fast response speed. AI algorithms have self-learning capabilities and can continuously accumulate new pest situation sample data, optimize recognition models, adapt to changes in pest species in different regions and seasons, and significantly reduce dependence on professional pest monitoring personnel. Support the combination of AI automatic analysis and manual correction mode. After the initial recognition results are generated by AI, the staff can review and correct the recognition results through the backend platform, further improving the recognition accuracy. For special pest species, samples can also be manually annotated to enrich database resources and enhance the system's ability to identify niche pests.
Multi dimensional data analysis to achieve accurate prediction and early warning. The backend platform of the system has powerful data analysis capabilities, which can perform multi-dimensional statistical analysis on monitoring data, including pest species distribution, quantity change trends, regional distribution characteristics, etc. By combining environmental parameters such as wind speed and direction, ground temperature and humidity, light intensity, and rainfall, the platform can comprehensively assess the risk of disease and pest occurrence, predict the trend of disease and pest spread, and provide scientific basis for precise prevention and control. When the number of pests exceeds the preset warning threshold, the system can automatically push warning information to the staff through platform pop ups, mobile text messages, and other means, guiding timely targeted prevention and control measures to avoid the widespread spread of pests and diseases. At the same time, it supports data export function and can generate detailed pest monitoring reports, providing data support for agricultural production management and pest control decisions.
Remote intelligent control enhances the overall management efficiency. The system supports multiple networking methods such as 4G, WIFI, and wired, enabling real-time remote transmission of pest data and device status. Staff can remotely view the working status of devices, real-time pest images, recognition results, and analysis reports through computers, mobile phones, and other terminals, achieving unified management of multi site devices across the entire area. Equipped with remote control function, it can remotely set device operating parameters (such as insecticidal temperature, processing time, photography frequency, working hours, etc.), trigger manual photography, switch working modes, greatly reducing operation and maintenance costs. Built in GPS positioning function, which can clearly view the distribution of various device stations in the map, facilitating unified scheduling and management of devices. The equipment has complete safety protection and environmental adaptation design, including lightning protection, leakage protection, rain control and drainage functions. It can operate stably in complex outdoor environments for a long time and is suitable for various scenarios such as large-scale planting bases, forestry protection areas, and agricultural research monitoring stations.