A precision millet farm uses 150 sensors/ha connected via IoT. If each sensor transmits 5 MB/hour and operates 16 hours/day, calculate total data volume generated per hectare per 30-day crop cycle. Discuss how ML algorithms can process this high-frequency data for anomaly detection in soil moisture and pest dynamics.
Data volume: 150 IoT sensors generate about 360,000 MB (~360 GB) over 30 days (5 MB/hour, 16 hrs/day).
• ML for soil anomalies: Use time-series and spatial methods (e.g., Isolation Forest, Autoencoders, change-point detection) to spot unusual moisture, pH, or nutrient patterns.
• ML for pest anomalies: Combine sensor signals (humidity, leaf-wetness, temperature) and possibly camera data with models (Random Forest, LSTM, clustering) to detect early pest risks and outbreak hotspots.
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