Smart Pest Monitor Product

Overview

A smart pest monitor is an autonomous trap equipped with a surveillance camera, motion detection, and cellular connectivity that captures images of trapped or approaching pests and sends real-time alerts to a user's smartphone or monitoring dashboard. The device combines traditional pest-control hardware (spring trap, bait chamber) with modern image processing and cloud connectivity to provide early warning and species identification without daily on-site inspection.

The system is deployed in agricultural fields, orchards, grain storage, or commercial facilities. When a pest approaches or enters the trap, the Camera Module captures an image, the Computing Core runs a machine-learning classifier to identify the pest species, and the Cellular Module sends the image, alert, and metadata to a cloud server. The user receives a notification on their mobile app with the pest type, timestamp, and location, enabling rapid response.

How it works

The Lure Holder emits an attractant—pheromone, food lure, or CO₂—that draws target pests toward the trap entrance. A PIR Motion Sensor with a Fresnel PIR Fresnel Lens detects motion, or a Trigger Contact Switch at the bait chamber triggers when a pest touches it. Either event wakes the Computing Core, a microprocessor running embedded Linux or RTOS.

The Camera Module then captures a high-resolution image: during daylight, the CMOS Image Sensor uses visible-light mode; at night, the Infrared LED Array array illuminates the scene and the IR Filter switches to IR-pass mode so the Lens Assembly captures the pest in monochrome IR. The image is compressed using H.264 or H.265 codec and temporarily buffered to the Storage Module eMMC.

The Main Processor loads a pre-trained neural-network model (stored locally) into the AI Accelerator (Optional) TPU and runs inference to classify the pest. Common models target aphids, beetles, grasshoppers, moths, spider mites, leafhoppers, and other crop pests. The classifier returns a species prediction with confidence; if confident, the image is tagged with metadata (species, timestamp, GPS from the app, trap ID). If uncertain, a full-resolution image is queued for cloud verification.

Simultaneously, the Trap Mechanism engages: a spring or solenoid Spring Mechanism snaps shut, capturing the pest in a Capture Cage. Optional Optional Kill Grid electrodes (3–6 kV, 100 mA pulse) deliver an instantaneous kill in high-sensitivity traps, or the trap can be designed to retain the pest alive for later identification by a technician.

The Cellular Module, powered by the Battery Pack (7.4–14.8 V Li-Po), initiates a connection to the LTE network via the Cellular Antenna. The classified image (or full image for verification) is uploaded to a cloud database along with trap metadata. A push notification is sent to the user's mobile app.

The Battery Pack capacity (5–20 Ah) and System RAM buffering strategy determine duty cycle: a trap that captures 1–5 pests per week can run 3–6 months on a single charge. Optional Solar Trickle Charger extends runtime to 6–12 months by trickle-charging during daylight. When power is low, the device enters deep-sleep mode and wakes only on motion or contact.

Integration and analytics

The cloud service aggregates trap data across multiple fields or facilities, displaying heatmaps of pest hotspots, population trends over time, and phenology curves. If pest pressure exceeds a user-defined threshold at a location, the app can trigger automated alerts for scouting or spray application. Integration with spray equipment and decision-support systems enables automated or assisted pesticide targeting.

The facial recognition or species-ID accuracy depends on the ML model quality and training dataset. State-of-the-art models achieve 85–95% species accuracy on crop pests; edge cases or cryptic species may require human verification via the cloud interface.