Ecological Monitoring Camera With AI Species Counting
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Solution Overview
Problem
Existing ecological data collection methods are time-consuming and labor-intensive, requiring trained individuals to manually visit and monitor ecological environments for species development, which can be hindered by weather and agricultural activities.
Innovation Solution
A data collection apparatus equipped with a camera and AI-driven machine learning model for autonomous, remote monitoring of organisms, capable of identifying species, counting plants, and collecting environmental data, including soil moisture and temperature, and configured to withstand outdoor conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data collection by trained individuals is used, then species identification accuracy is improved, but data collection time and labor intensity increase
Solution Approach 1:
The patent replaces the mechanical system of manual visual inspection by trained individuals with an automated optical system using a camera and image processing algorithm. The system captures images of ecological environments and automatically identifies species characteristics through computational analysis, eliminating the need for continuous human presence while maintaining identification accuracy.
Solution Approach 2:
The patent introduces an intermediary computational processing layer between the ecological environment and the data collection output. The image processing algorithm acts as a mediator that analyzes captured images to extract species identification data, count organisms, and measure characteristics, thereby automating the data collection process without requiring direct human intervention in the field.
2Measurement precision
If manual data collection by trained individuals is used, then data quality is improved, but labor intensity increases
Solution Approach 1:
The patent replaces the human labor system with an automated imaging and processing system. The camera and image processing algorithm together form a device that automatically performs species identification, counting, and measurement tasks previously requiring trained individuals, thereby reducing labor intensity while maintaining data quality through systematic computational analysis.
3Duration of action of moving object
If regular visits to ecological environments are required, then species development tracking is improved, but disruption from weather and agricultural activities increases
Solution Approach 1:
The patent implements a self-service monitoring system where the camera and image processing algorithm autonomously capture and analyze ecological data without requiring human visits. The system independently tracks species development over time by periodically capturing images and processing them to extract relevant information, thereby eliminating disruptions from weather conditions and agricultural activities associated with manual field visits.
Solution Approach 2:
The patent replaces the mechanical system of periodic human field visits with an automated stationary or mobile imaging system. The camera system continuously or periodically captures ecological data and the image processing algorithm analyzes the images to track species development, enabling long-term monitoring without human exposure to adverse weather or agricultural operations.
Data Source
AI summary
Disclosed are various embodiments for a data collection apparatus that is configured to detect, count, and/or identify organisms and their characteristics from a media item (e.g., an image, a video, etc.) of one or more ecological environments. For example, a system can include a camera for capturing an image of an ecological environment and a computing device. The computing device can be configured to at least identify a triggering condition for the ecological environment and capture, using the camera, an image of the ecological environment based at least in part on the triggering condition. The computing device can determine a characteristic of an organism in the ecological environment based at least part in a machine learning model.