Convolutional Neural Network for Population Deviation Calculation
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Solution Overview
Problem
Conventional pest detection methods in agricultural and industrial settings, such as beekeeping, rely on labor-intensive visual inspections and often result in unnecessary chemical treatments due to inaccurate estimations of pest infestations, leading to inefficiencies and environmental pollution.
Innovation Solution
A method using a combination of deep and shallow convolutional neural networks to automatically calculate the deviation relation of a population registered on an image, identifying objects and abnormalities, such as varroa mites in bee populations, by analyzing color, reflectivity, and shadows, and providing a more accurate assessment of infestation levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional visual inspection methods are used to detect pests, then labor costs are high, but measurement precision of pest infestation levels is insufficient leading to inaccurate estimations
Solution Approach 1:
The patent replaces manual visual inspection with an automated image processing system using convolutional neural networks. The system captures images of the population and automatically detects abnormalities, substituting human labor with computational algorithms that provide both high precision in detection and improved productivity through automation.
Solution Approach 2:
The patent introduces an image processing system as an intermediary between the population and the detection process. The system uses captured images as intermediate data to analyze pest infestation levels, enabling accurate measurement without direct human inspection while maintaining high productivity.
2Reliability
If pesticide is applied regardless of pest presence to ensure control, then pest management reliability is improved, but loss of substance increases due to wasteful chemical usage
Solution Approach 1:
The patent implements a feedback-based pest management system where the image processing results directly inform treatment decisions. The system provides accurate feedback on actual pest infestation levels, enabling targeted pesticide application only when and where needed, thus maintaining control reliability while minimizing chemical waste.
Solution Approach 2:
The patent changes the decision parameter for pesticide application from fixed schedules or arbitrary thresholds to dynamically determined infestation levels based on image analysis. This allows treatment thresholds to be adjusted according to actual pest presence, reducing unnecessary chemical application while ensuring treatment when infestation exceeds acceptable levels.
Data Source
AI summary
A method of calculating the deviation relation of a population registered on an image includes: i.) identifying the objects in the image, ii.) estimating the number of identified objects, iii.) identifying abnormalities in the image, iv.) identifying objects with abnormalities in the image, v.) estimating the number of objects with abnormalities, vi.) calculating the relation of objects with abnormalities to all objects. A computer program, a handheld computer device, and a system are also provided.