For small-scale mushroom farmers, contamination is a constant, silent threat. You review sensor logs, but correlating yesterday's humidity spike with today's worrying patch feels like guesswork. What if your environmental data could proactively warn you?

Your First Model: A Baseline Risk Framework

The core principle is to move from raw data to calculated risk features . Don't just look at average conditions; analyze the patterns that stress your crop. Transform daily sensor streams into a structured table where each row represents one growing block or day, and columns are specific, calculated metrics derived from your e-book's facts.

These features fall into clear categories:

Averages: Avg_Temperature , Avg_Relative_Humidity .

Extremes & Variability: Max_Temperature , Temperature_Swing (Max-Min). Large swings are often riskier than steady, slightly off temps.

Duration-Based Metrics: Hours_Above_Humidity_Threshold (e.g., >90%).…