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AgriSense Intelligence Hub: Production Technical Report
Developed by: Karthik Reddy (230035) & Akash G (230098)
🚀 System Overview: Integrated Agricultural Intelligence
AgriSense AI is a unified decision-support platform designed to stabilize agricultural incomes by bridging the gap between market volatility and ecological suitability. The system utilizes a Decoupled Engine Pattern, allowing for modular inference across price forecasting, crop recommendation, and disease diagnostics.
1. The Production Forecasting Strategy
The platform implements a dual-track forecasting architecture to provide both point-precision and risk quantification:
Track A: Temporal Fusion Transformer (TFT) — THE PRODUCTION STANDARD
- Accuracy (SMAPE): 96.15% (3.85%)
- RMSE: 155.96
- MAE: 78.89
- Momentum Signal: +1.0 (High Confidence)
- Horizon: 14-Day Multi-Step
- Architectural Scaling:
- Hidden Size: Increased from 16 to 128 (8x capacity).
- Context Window: Expanded from 14 to 60 days (Deep lookback).
- Dropout: Optimized to 0.20 for generalization.
- Training: Finalized at Epoch 15.
- Reasoning: By utilizing Self-Attention and Gated Residual Networks, the TFT identifies long-range dependencies and supply shocks, providing probabilistic quantile corridors for risk analysis.
Track B: LightGBM (GOSS-Optimized) — THE BENCHMARK
- Accuracy (SMAPE): 91.20%
- Inference Speed: < 100ms per mandi-commodity pair.
- Reasoning: Handles high-cardinality tabular features (Mandi/District mapping) with extreme efficiency, serving as the high-speed point-forecaster for real-time dashboard interactions.
2. High-Dimensional Ecological Intelligence
- Module: Crop Recommendation Engine
- Accuracy (F1-Score): 0.99
- Feature Engineering: 45 composite soil-climate markers, including nutrient ratios ($N:P:K$), heat indices, and aridity proxies.
- Unified Logic: Biologically viable crops are filtered through the 14-day price forecasting track to recommend only the most profitable cultivation paths.
3. Biological Risk Radar (Computer Vision)
- Architecture: EfficientNet-B0 (Transfer Learning)
- Dataset Scale: 87,000+ images across 38 classes.
- Accuracy: 98.42%
- Interpretability: Integrated Grad-CAM heatmaps to visualize biological triggers, providing transparency for agronomist auditing.
🛠 Architecture: The Decoupled Hub
The system is deployed via a Next.js Production Dashboard communicating with a FastAPI Modular Hub.
- Modular Engines: All models are encapsulated in a canonical
Engines/interface, allowing for model weights to be updated (e.g., from Epoch 9 to Epoch 15) without changing the API logic. - Data Fusion: Automated Agmarknet scraping and NASA POWER API integration ensure the models are always served with the latest meteorological and market data.
📊 Performance Summary
| Module | Model | Metric | Result |
|---|---|---|---|
| Price Forecasting | TFT (Epoch 15) | Accuracy | 96.15% |
| Price Forecasting | LightGBM | SMAPE | 8.80% |
| Crop Recommendation | GBDT | F1-Score | 0.99 |
| Plant Disease | EfficientNet-B0 | Accuracy | 98.42% |
🎓 Technocratic Assessment
Q: Why does the system use a hybrid of TFT and LightGBM? A: While LightGBM is faster for deterministic point-prediction, the TFT provides essential uncertainty quantification (Quantiles). In agriculture, knowing the 'worst-case' price (P10) is often more valuable than a single average prediction.
Q: How is data noise handled? A: We use a 30-day sequence lookback and rolling volatility markers. This dampens short-term sensor or reporting noise while allowing the model to stay sensitive to genuine market momentum.
Q: What is the benefit of the Decoupled Architecture? A: It allows the system to scale. New models (e.g., foundation models like Moirai) can be added as new Engines in the Hub without requiring a frontend or API rewrite.