Machine Learning Model for Adaptive Livestock Feed Selection
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Solution Overview
Problem
Current livestock management methods are manual, time-consuming, and costly, requiring specialist intervention to optimize feed blends and medicinal treatments for improving animal health and bioproduct quality, which is inefficient and prone to errors.
Innovation Solution
A machine learning-based system that trains models to receive health status and bioproduct quality inputs to automatically generate feed selections, adjusting hyperparameters based on real-time data to improve health and production outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual livestock management with specialist consultation is used, then animal health and bioproduct quality can be improved, but time consumption and operational costs increase significantly
Solution Approach 1:
The system enables self-service by implementing automated machine learning models that independently analyze livestock health data and generate feed recommendations without requiring specialist intervention. The ML model processes health status inputs and automatically outputs optimized feed selections, allowing the system to serve itself rather than relying on external expert consultation.
Solution Approach 2:
The patent replaces the mechanical system of manual specialist consultation with an automated computational system. Machine learning algorithms substitute for human veterinarians in analyzing health data and making feed recommendations, transforming a labor-intensive process into an automated digital workflow that reduces time loss while maintaining or improving decision quality.
2Reliability
If manual livestock management is used, then expert decisions can be made, but operational costs and resource waste increase
Solution Approach 1:
The system performs self-service by automatically generating feed recommendations through machine learning models without requiring expensive specialist consultations. The ML model independently processes health data and produces optimized feed selections, eliminating the need for continuous external expert involvement and thereby reducing operational costs while maintaining decision quality.
Solution Approach 2:
The patent employs cost-effective computational resources instead of expensive human specialists. Machine learning models run on affordable computing infrastructure to generate feed recommendations, replacing the need for repeated payments to high-cost veterinary services while maintaining or improving the quality of feed selection decisions.
3Reliability
If manual management processes are used, then specialist knowledge can be applied, but error rates and inefficiency increase
Solution Approach 1:
The system substitutes manual mechanical processes of data collection and analysis with automated digital systems. Machine learning models continuously process health data and generate feed recommendations without human intervention, eliminating errors associated with manual data handling while improving management efficiency through rapid automated decision-making.
Solution Approach 2:
The system implements feedback loops where the ML model continuously receives health status data, generates feed recommendations, and learns from the outcomes to improve future decisions. This automated feedback mechanism enhances decision accuracy over time while maintaining high management efficiency, as the system automatically adjusts based on observed results without requiring manual review.
4Reliability
If specialist consultation is required for each livestock issue, then quality of care can be maintained, but scalability to large livestock populations becomes difficult
Solution Approach 1:
The machine learning model serves multiple functions simultaneously: it monitors health status, analyzes data patterns, generates feed recommendations, and adapts to different livestock types. This universal system can handle diverse livestock populations with a single platform, enabling scalability while maintaining consistent quality of care across all animals through standardized automated decision-making.
Solution Approach 2:
The system provides self-service capabilities that allow it to independently manage large livestock populations without proportionally increasing specialist involvement. The ML model automatically processes health data and generates recommendations for any number of animals, enabling the system to scale to large populations while maintaining quality care through automated rather than manual processes.
Data Source
AI summary
Embodiments disclosed include systems, apparatus, and/or methods to receive a target health status of and/or quality of bioproduct produced by a managed livestock and indications of health status and quality of bioproduct. The systems, apparatus, and/or methods generate a set of input vectors based on the target health status or bioproduct quality, and the indications of bioproduct quality, and health status, and provide the set of input vectors to a machine learning model trained to generate an output indicating a feed selection. The feed selection can be included in a feed blend and administered to the managed livestock, such that, upon consumption, it increases a likelihood of collectively improving the health status of the managed livestock and the bioproduct quality of the managed livestock.