Fowl Health Monitoring via Image-to-Weight Formula
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for monitoring the health of domesticated fowls are labor-intensive and time-consuming, leading to difficulties in reducing breeding and maintenance costs and poor monitoring efficiency, especially in tropical regions where heat stress affects fowl growth and egg quality.
Innovation Solution
A domesticated fowl health monitoring system that includes a learning calibration module with a weighing structure and cameras, a computing core for image analysis, and a cloud module for data storage and analysis, using deep learning algorithms to automatically detect weight and activity, and an early warning system for rapid response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual detection methods are used to monitor fowl health and eating behavior, then the system is simple and easy to operate, but it consumes a lot of manpower and time, leading to poor monitoring efficiency
Solution Approach 1:
The patent replaces manual mechanical detection with an automated image processing system. Cameras capture images of fowl at feeders, and image processing algorithms automatically analyze eating behavior, replacing the need for manual observation and recording. This substitution dramatically improves monitoring efficiency while reducing labor requirements.
Solution Approach 2:
The system enables self-service monitoring where the fowl themselves are monitored passively through their natural interaction with automated feeders equipped with sensors. The system automatically detects weight changes, eating patterns, and health status without requiring active participation or intervention, improving efficiency while maintaining operational simplicity.
2Productivity
If traditional manual weight measurement and health evaluation methods are used for each fowl, then measurement precision can be maintained, but it requires a lot of manpower and cannot improve processing speed
Solution Approach 1:
The patent replaces manual weight measurement with automated image processing technology. Cameras capture images of individual fowl, and deep learning algorithms automatically estimate weight and health status from these images. This substitution maintains measurement precision through advanced computer vision while dramatically improving processing speed by eliminating manual intervention for each fowl.
Solution Approach 2:
The system creates visual copies (images) of fowl at different time points and uses these copies for analysis instead of physically handling the animals. This allows rapid, non-intrusive measurement of weight and health parameters while maintaining accuracy through image analysis algorithms, thereby improving processing speed without sacrificing measurement precision.
3Measurement precision
If indirect indicators like temperature and humidity index are used to evaluate heat stress, then the monitoring system is simple, but the evaluation may be incorrect due to breed and diet variations
Solution Approach 1:
The patent applies local quality by tailoring the monitoring and evaluation criteria to specific fowl breeds, diets, and environmental conditions. Instead of using universal temperature-humidity thresholds, the system learns breed-specific and diet-specific patterns through machine learning, improving heat stress evaluation accuracy for each local context while adapting the system complexity to match the specific requirements.
Solution Approach 2:
The system dynamically adjusts evaluation parameters based on breed, diet, and environmental conditions rather than using fixed thresholds. Machine learning models learn optimal parameter ranges and heat stress indicators specific to each fowl population and condition, improving measurement precision while the automated nature of the system manages the complexity of multiple variable parameters.
4Productivity
If automated image processing and deep learning systems are implemented for fowl monitoring, then monitoring efficiency and productivity are improved, but the device complexity increases
Solution Approach 1:
The patent implements a universal monitoring system that performs multiple functions: weight estimation, health status monitoring, eating behavior analysis, and heat stress detection, all through a single integrated image processing platform. This multi-functionality improves productivity by consolidating multiple monitoring tasks into one system while managing complexity through shared hardware and software resources.
Solution Approach 2:
The system performs preliminary actions by pre-training deep learning models with large datasets of fowl images and characteristics before deployment. This preliminary training enables the system to quickly and accurately analyze new images without requiring complex real-time processing, thereby improving monitoring efficiency while the model training complexity is handled offline during system setup.
Data Source
AI summary
A domesticated fowl health monitoring system includes a learning calibration module, a computing core, a cloud module, and a monitoring module. The learning calibration module is configured to detect a weight of at least one first domesticated fowl and generate a first domesticated fowl image. The computing core is configured to analyze a number of the at least one first domesticated fowl and generate a domesticated fowl image feature and an image-to-weight formula. The cloud module is configured to store the domesticated fowl image feature and the image-to-weight formula. The monitoring module is configured to generate a second domesticated fowl image presenting at least one second domesticated fowl. The cloud module is further configured to obtain a unit weight of the at least one second domesticated fowl based on the second domesticated fowl image and the image-to-weight formula. The present disclosure further provides a domesticated fowl health monitoring method.