Livestock Digital Twin for Predictive Value Chain Analysis
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Solution Overview
Problem
In the livestock value chain, entities such as farmers, breeders, and abattoirs face challenges in predicting market demand, livestock health, and growth due to manual processes, scattered locations, and communication issues, which affect decision-making and efficiency.
Innovation Solution
A machine learning system that digitizes livestock through image processing, training models for abattoir readiness, supply and demand forecasting, and environmental footprint analysis, using data from various sources like weather, GPS, and health records to provide predictive insights and optimize decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processes are used for livestock management, then operational simplicity is maintained, but predictive accuracy for market demand, livestock health, and growth deteriorates
Solution Approach 1:
The patent creates digital representations (copies) of livestock using image processing and pixel assignment. These digital twins capture physical characteristics and enable machine learning analysis without requiring direct physical measurement, thereby improving predictive accuracy while maintaining operational simplicity through virtual modeling.
Solution Approach 2:
The patent replaces manual mechanical assessment processes with machine learning models that analyze digital representations. The system substitutes human judgment and physical measurement with automated computational analysis, improving prediction accuracy for market demand, health status, and growth trajectories.
2Loss of information
If scattered locations and communication issues are present, then operational flexibility is maintained, but information availability and decision-making efficiency deteriorate
Solution Approach 1:
The patent creates a universal digital representation system that can be applied across diverse locations and livestock types. The machine learning model processes standardized digital representations regardless of geographic location, ensuring consistent information availability while maintaining the flexibility to operate in scattered environments through location-independent digital modeling.
Solution Approach 2:
The patent introduces digital representations as an intermediary between physical livestock and information systems. This intermediary layer captures and transmits essential information about livestock status, health, and characteristics, bridging communication gaps between scattered locations and enabling centralized analysis without compromising operational autonomy.
3Productivity
If machine learning models are trained using digital representations, then predictive capability is improved, but data processing requirements and computational resources increase
Solution Approach 1:
The patent extracts only the essential pixels and characteristics needed for prediction from complete images. By assigning specific pixel sets to represent livestock rather than processing entire images, the system reduces data volume and computational energy requirements while maintaining sufficient information for accurate predictions about market demand, health, and growth.
Data Source
AI summary
Methods and systems for operating a machine learning system are described. In an example, a device can receive an image and assign a set of pixels in the image as a digital representation of a livestock. The device can further train a machine learning model using the digital representation. The device can further run the machine learning model to generate prediction data relating to the livestock. The device can further generate output data relating to at least one activity among a livestock value chain. The at least one activity can correspond to a process to generate a commodity based on the livestock.