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.