---
tags:
- time-series
- agriculture
- forecasting
- tabular
---

# crop_yield_prediction_transformer

## Overview
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.

## Model Architecture
The architecture is based on a standard Encoder-Decoder Time-Series Transformer.



- **Input Embedding:** Maps multi-variate features (temperature, rainfall, soil pH, nitrogen levels) into a dense vector space.
- **Positional Encoding:** Injected to maintain the temporal order of growing seasons.
- **Decoder:** Generates a 15-day prediction horizon based on a 30-day context window of environmental data.

## Intended Use
- **Precision Agriculture:** Helping farmers optimize fertilizer and water usage.
- **Food Security:** Enabling governments to forecast domestic food production levels.
- **Crop Insurance:** Providing data-driven risk assessment for insurance payouts based on climatic anomalies.

## Limitations
- **Extreme Weather Events:** Black swan events like sudden locust swarms or unprecedented floods are not captured by historical patterns.
- **Local Specificity:** A model trained on European wheat data will not generalize to tropical rice paddies without extensive fine-tuning.
- **Static Features:** Assumes consistent farming practices; sudden changes in technology or equipment are not modeled.