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Akasha · AI Core
CIDSA Smart Crop Analytics AI
Models that read every leaf.
Module Role
AI-driven crop insights and predictions
About this module
What it does
Smart Crop Analytics AI runs computer-vision and time-series models across satellite, drone, and ground-image streams to detect anomalies the human eye misses. Output is one number per block: a crop-health index updated daily, with the contributing factors broken out so the agronomist knows what to fix.
Key features
- Daily crop-health index per block
- Multispectral anomaly detection
- Phenology stage classification from imagery
- Yield-prediction model fed by current health
- Explainable AI — every score traces to a signal
CIDSA Analytics · Akasha
Model Confidence & Insights
60 farms · 1.4 M observations
This SeasonAll BlocksIndia
Model AUC
0.93
health classifierYield MAE
6.4%
per blockAnomaly lead
9.2 days
vs. visibleFalse alarms
< 3%
Crop health index · 60-day trend
0-100 across 4 sample blocks
- Block A
- Block B
- Block C
- Block D
Anomaly type mix
Last 90 days
- Disease
- Nutrient
- Other
- Pest
- Water
Predicted vs. realised yield
q/acre · 12 blocks
- Actual
- Predicted
Sample simulation data · Live data feeds available in deployment.Powered by CIDSA Data Visualization & Analytics
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LifecycleCIDSA Crop Lifecycle Management Pro
End-to-end crop lifecycle tracking