Deep Learning Insect Identification for Bee Tracking and Counting
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Solution Overview
Problem
Conventional technologies for managing beehives and greenhouses require sensor installations that disrupt bee environments and lack the ability to track bee trajectories, analyze pollination activities, or identify unwanted insects like hornets and ants.
Innovation Solution
An apparatus and method using a deep learning model to track bee trajectories, count bee entries and exits, assess pollination suitability, and identify honeybees, bumblebees, hornets, ants, and grasshoppers by analyzing morphological characteristics and 3D distance measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are installed in beehives to detect bumblebee entries, then the number of bee individuals can be counted, but the bee's environment is affected and additional functionality is limited
Solution Approach 1:
The invention extracts the detection function from physical sensors inside the beehive and relocates it to an external camera system. The camera is positioned outside the beehive to capture images of bees entering and exiting, eliminating the need to install sensors within the hive environment that would disturb the bees.
Solution Approach 2:
The invention introduces an intermediary processing system that includes a processor trained with deep learning models. This intermediary processes the images captured by the external camera to identify and count bees, replacing the direct sensor-bee interaction with an indirect optical measurement approach that does not affect the bees.
2Quantity of substance
If conventional sensor systems are used, then basic bee counting is achieved, but trajectory tracking and pollination analysis capabilities are lacking
Solution Approach 1:
The invention performs preliminary action by training the processor with deep learning models before actual bee detection. The processor is pre-trained with images of various bee species and characteristics, enabling it to not only count bees but also identify species, track trajectories, and analyze behavior patterns from the captured images.
Solution Approach 2:
The invention makes the detection system universal by enabling it to perform multiple functions: counting bees, identifying species, tracking trajectories, analyzing pollination behavior, and detecting abnormal insects. The single camera system with processed image analysis replaces multiple specialized sensors, providing comprehensive monitoring capabilities.
3Measurement precision
If deep learning models are trained with morphological characteristics, then accurate insect identification is achieved, but computational complexity increases
Solution Approach 1:
The invention segments the deep learning model into specialized components: one trained specifically for bee species identification and another trained for abnormal insect detection. This segmentation allows each model to focus on specific morphological characteristics, improving accuracy while managing computational complexity through specialized rather than general-purpose processing.
Data Source
AI summary
An apparatus and a method for identifying insect objects, particularly honeybees, bumblebees, hornets, ants, and other insects, based on their physical structures, colors, and other characteristics learned or trained through a deep learning model are proposed.