Livestock Body Weight Estimation via Machine Learning
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Solution Overview
Problem
Current methods for estimating the body weight of livestock are labor-intensive and inefficient, particularly for large-scale farming, as they require individual measurement and do not account for factors like muscle, fat, bone, and blood density, which affect meat quality and accuracy.
Innovation Solution
A system utilizing a client terminal and estimation apparatus that employs machine learning models based on captured image data and collected animal data to estimate body weight, incorporating identification information and activity data, allowing for remote and accurate weight estimation without direct animal contact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If body weight measurement is performed on an animal-by-animal basis using traditional methods, then measurement accuracy is improved, but labor burden and time consumption increase significantly
Solution Approach 1:
The patent uses image data (visual copy) of livestock to create a digital representation that can be processed by machine learning models to estimate body weight, replacing the need for physical weighing of each animal. This allows multiple animals to be measured simultaneously through imaging while maintaining estimation accuracy through the trained model.
Solution Approach 2:
The patent replaces mechanical weighing systems with an information processing system that uses image analysis and machine learning. The mechanical act of placing animals on scales is substituted by capturing images and processing them through algorithms, significantly reducing labor while providing body weight estimates.
2Measurement precision
If machine learning models process multiple factors (muscle, fat, bone, blood density), then estimation accuracy is improved, but system complexity increases
Solution Approach 1:
The patent merges multiple data sources (image data, activity data, and other collected animal data) into a unified machine learning model. By combining these different types of information, the system comprehensively accounts for muscle, fat, bone, and blood density factors without requiring separate complex measurement systems for each factor.
Solution Approach 2:
The machine learning model automatically processes and integrates multiple factors (muscle, fat, bone, blood density) without requiring manual intervention or separate measurement procedures for each factor. The system self-services by autonomously analyzing the comprehensive data to produce accurate body weight estimates.
Data Source
AI summary
An estimation apparatus includes an acquisition unit configured to acquire data about a domesticated animal identified by identification information that is transmitted by a client terminal, and an estimation unit configured to perform estimation by inputting the acquired data about a domesticated animal to a trained model generated by performing machine learning based on captured image data of domesticated animals and collected data about domesticated animals and provide, to the client terminal, an estimation result indicating a result of the estimation, and the client terminal includes a presenting unit configured to transmit a request for estimation together with identification information for identifying a domesticated animal targeted for estimation, receive the estimation result, and present body weight data about the domesticated animal targeted for estimation to a user.