You’ve logged sensor data for months, but connecting those numbers to actual contamination events feels like guesswork. By the time you spot a problem, it’s often too late. What if you could turn that historical data into a daily, predictive risk alert?
Building Your First Baseline Algorithm
The core principle is moving from reactive observation to proactive, data-driven prediction. You do this by transforming raw sensor logs into a structured, labeled dataset where each day’s environmental conditions are linked to a known outcome—like a clean flush or a contamination event. This becomes the foundation for a simple, baseline risk algorithm.
The key is calculating specific, actionable features from your raw Avg_Temperature, Avg_Relative_Humidity, and Avg_CO2 data. Crucially, you must go beyond averages. Incorporate duration-based metrics like Hours_Above_Humidity_Threshold to capture prolonged wetness, a major risk. Also, calculate extremes & variability, such as Temperature_Swing (Max - Min), because large fluctuations stress mycelium more than a steady, slightly off temperature.
Mini-Scenario: Your system calculates a day’s Temperature_Swing as 8°C and Hours_Above_Humidity_Threshold as 14. Your baseline model, trained on past data where these patterns preceded Trichoderma, flags a HIGH RISK alert, prompting a climate check before contamination is visible.
Your Implementation Pathway
- Compile and Label Your Data: Gather 6+ months of sensor and production logs. For each growth cycle, label periods as HIGH RISK (linked to past contamination) or LOW RISK (successful flushes).
- Calculate Your Feature Set: For each labeled period, programmatically calculate the checklist of key features: the averages, extremes, swings, and critical duration metrics.
- Deploy a Daily Report: Use a low-code platform like Google Vertex AI AutoML to train a simple classification model on your features. Integrate its logic into a daily workflow that ingests new sensor data, calculates the same features, and outputs a risk score with the top contributing factors.
By implementing this baseline, you shift from data collection to intelligent analysis. You gain a consistent, daily risk assessment that highlights dangerous environmental patterns, allowing for timely intervention. Start with your historical data, focus on these key features, and build your first predictive layer of farm intelligence.
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