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--- |
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tags: |
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- time-series |
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- agriculture |
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- forecasting |
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- tabular |
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--- |
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# crop_yield_prediction_transformer |
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## Overview |
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A specialized Time-Series Transformer model built to predict agricultural crop yields based on historical growth patterns, soil moisture sensors, and meteorological forecasts. It outputs a probabilistic distribution of expected yield (tonnes/hectare) for the upcoming harvest cycle. |
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## Model Architecture |
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The architecture is based on a standard Encoder-Decoder Time-Series Transformer. |
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- **Input Embedding:** Maps multi-variate features (temperature, rainfall, soil pH, nitrogen levels) into a dense vector space. |
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- **Positional Encoding:** Injected to maintain the temporal order of growing seasons. |
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- **Decoder:** Generates a 15-day prediction horizon based on a 30-day context window of environmental data. |
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## Intended Use |
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- **Precision Agriculture:** Helping farmers optimize fertilizer and water usage. |
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- **Food Security:** Enabling governments to forecast domestic food production levels. |
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- **Crop Insurance:** Providing data-driven risk assessment for insurance payouts based on climatic anomalies. |
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## Limitations |
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- **Extreme Weather Events:** Black swan events like sudden locust swarms or unprecedented floods are not captured by historical patterns. |
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- **Local Specificity:** A model trained on European wheat data will not generalize to tropical rice paddies without extensive fine-tuning. |
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- **Static Features:** Assumes consistent farming practices; sudden changes in technology or equipment are not modeled. |
