Livestock Health Monitoring System
Project Overview
TThis use case demonstrates how the Artemis Planning Agent transformed a simple idea—
“I want to build a cattle health monitoring system using a neck sensor.”
—into a complete livestock health intelligence platform.
Starting from a raw concept, Artemis acted as a technical architect, research assistant and a developer, uncovering requirements, exploring scientific literature, and shaping a multi-layer intelligence system capable of detecting behavior, analyzing physiological patterns, and generating real-time health insights for farmers.
Goal
Build a system that automatically interprets neck-mounted sensor data and produces meaningful health insights, alerts, and behavioral understanding in real time. We just have a single sentence idea with raw sensor readings (accelerometer, gyroscope, temperature) and no defined pipeline.
Planning Process
Artemis transformed the problem definition into a production-ready design.
How Artemis Clarified the Requirements
When provided with the initial statement, Artemis immediately began refining the problem. It asked clarifying questions to understand:
- What sensors exist
- What types of insights matter
- Whether data should be processed live, in batch, or both
- What kind of output and interface is expected
- Whether we need explainability and personalized baselines
- Operational constraints such as sampling rate and storage
Through this clarification loop, Artemis gathered enough information to design the three-layer intelligence architecture:
- Physical Behavior
- Physiological Analysis
- Health Intelligence
This structure emerged naturally from Artemis interpreting the collected requirements and shaping them into a clean, scalable design.
Artemis as a Research Assistant
After defining the problem, Artemis automatically explored the scientific and technical space to determine the best approach.
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Artemis researched:
Artemis investigated how animal posture can be inferred from accelerometer angles, how walking patterns emerge through acceleration variance, the established temperature thresholds for fever and estrus, the influence of circadian rhythms on body temperature, and how combining temperature with movement data can reduce false positives. This allowed the system to be grounded in validated physiological and behavioral principles.
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Artemis discovered datasets:
Including publicly available cattle behavior datasets (e.g., Japanese Black Beef Cow Dataset), helping validate the chosen approaches.
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Artemis reviewed UI/UX patterns:
Recommending alert-first dashboards, historical trends, and simple CSV upload entry points.
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Artemis selected the right methodology:
A hybrid model — simple rules for posture, ML for complex motion, and rolling statistical models for physiology.
The result is a system grounded in real science, best practices, and industry patterns — all gathered autonomously by Artemis.
System Architecture
Artemis synthesized all requirements and research into a three-layer intelligence pipeline:
Input (CSV or Real-time MQTT)
↓
Layer 1 — Behavior Classification
(lying, standing, walking, feeding, activity level)
↓
Layer 2 — Physiological Analysis
(temperature baseline, trends, circadian adjustments)
↓
Layer 3 — Health Intelligence
(alerts, scoring, events, estrus & fever detection)
↓
Output — Dashboard + SQLite DB
Each layer is modular and feeds into the next, allowing independent updates and easier debugging.
Dual Processing Modes
Artemis identified the need for two complementary ways of processing data:
Batch Processing (implemented first)
This workflow supports CSV uploads, enabling Full 3-layer analysis, stored results in SQLite and dashboard-ready insights.
It provided a stable environment for testing and validating logic.
Real-Time Streaming (added later)
Once batch mode was complete, we used the “build new feature” buit-in prompt of Artemis planner to add real time analysis support.
Artemis added MQTT ingestion, Continuous event processing, Live alert generation and Real-time dashboard updates.
The live pipeline reused the same intelligence layers designed earlier, demonstrating Artemis’s ability to extend systems progressively without re-architecting.
Final Capabilities
Behavior Detection
- Lying, standing, walking, feeding
- Movement intensity
- Rest duration
- Stress-related motion
Temperature & Physiology
- Rolling baselines
- Z-score anomalies
- Circadian adjustments
- Temperature–activity correlation
- Trend monitoring
Health Intelligence
- Fever, heat stress, estrus signals
- Prolonged inactivity alerts
- Sensor malfunction detection
- 0–100 health scoring
Dashboard & Storage
- Real-time status
- Historical trends
- Alerts timeline
- SQLite database backend
Demo
Dashboard page
Alerts Page
Health analysis page
