Wild Insect Monitoring With Motion-Triggered AI Detection

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Solution Overview

Problem

Insect monitoring in crop management is labor-intensive and inefficient, especially in large-scale farming operations, requiring significant manual effort to detect and identify insects.

Innovation Solution

A computer-implemented method using an artificial intelligence model trained via unsupervised domain adaptation techniques to automatically detect and identify insects, utilizing cameras and motion sensors to capture images, and a system that includes a computing device for analyzing insect data and recommending or implementing a course of action.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

If manual insect monitoring is used, then labor flexibility and adaptability are maintained, but labor intensity and time consumption increase significantly

Engineering Contradiction:

Improveautomation of insect detectionVSAvoidsystem complexity

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The patent replaces manual mechanical inspection with an automated computer vision system that uses cameras, motion sensors, and AI algorithms to detect and identify insects, eliminating the need for human labor in the field while reducing system complexity through standardized automated processes

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system performs self-monitoring by automatically detecting insect movement through motion sensors, capturing images via cameras, processing data through embedded AI models, and generating pest alerts without requiring external human intervention, thereby achieving autonomous operation

Inventive Principle:

Principle #25Self-service

2Productivity

If manual insect monitoring is used, then operational simplicity is maintained, but productivity and monitoring coverage decrease

Engineering Contradiction:

Improvemonitoring efficiencyVSAvoidoperational simplicity

Core Design Contradiction:

ProductivityVSEase of operation

Solution Approach 1:

The patent replaces manual inspection with automated computer vision technology that uses cameras and AI algorithms to rapidly detect and identify insects, dramatically increasing monitoring productivity and coverage while maintaining ease of operation through automated decision-making

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system introduces an intermediary AI processing layer that automatically analyzes captured images, identifies pest species, determines infestation levels, and generates recommendations, thereby increasing productivity without complicating user operation as the AI handles complex analysis tasks

Inventive Principle:

Principle #24Intermediary (Mediator)

3Measurement precision

If manual insect identification is used, then accuracy can be maintained through expert knowledge, but time consumption and labor requirements increase

Engineering Contradiction:

Improveinsect identification accuracyVSAvoidtime for insect detection

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces manual expert identification with AI-based computer vision systems that use trained neural networks to accurately identify insect species and characteristics, achieving expert-level precision while reducing detection time through automated parallel processing of multiple images

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system performs preliminary actions by pre-training AI models on extensive datasets of insect images and characteristics, enabling rapid accurate identification during actual monitoring without requiring time-consuming real-time analysis, thus maintaining high accuracy while reducing detection time

Inventive Principle:

Principle #10Preliminary action

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

Automates insect detection and identification, reducing labor requirements and improving efficiency in crop management by providing real-time data for precise pest control measures, thereby reducing costs and crop damage.

Implementation Method 1

a motion sensor communicably coupled to one or more cameras and operable to signal the one or more cameras to initiate image capture in response to detection of insect movement into the insect imaging zone

Methodology Applied

Scientific EffectMotion detection:

Data Source

PatentUS20250299512A1Sensor-based smart insect monitoring system in the wild

Publication Date: 2025.09.25 SOLARID LLC

AI summary

Embodiments of the present disclosure pertain to a computer-implemented method of insect monitoring by capturing at least one image of one or more insects; transmitting the at least one image to a computing device, where the computing device includes an artificial intelligence model operable to identify insects, and where the artificial intelligence model is trained on previously collected insect images via an unsupervised domain adaptation technique; and utilizing the artificial intelligence model to generate insect data related to the one or more insects from the at least one image. Additional embodiments of the present disclosure pertain to a system for insect monitoring.