Cloud-Native Deep Learning · Precision Agriculture

Predict Crop Yield Across
Genotype × Environment × Management

A cloud-native platform that fuses genomic, climatic, and agronomic data through deep learning to deliver high-fidelity yield predictions with uncertainty quantification.

R² = 0.93

Across 12 environments

< 0.41 RMSE

t/ha yield error

8 Crops

Maize · Wheat · Soy · Rice ···

Platform Capabilities

Six integrated modules spanning genomics, climate modeling, agronomy, and explainable AI.

🧬GAT

Genotype Graph Encoder

Graph Attention Network over SNP-marker graphs captures epistatic interactions across chromosomal loci.

🌦️Transformer

Environment Transformer

Multi-head temporal attention over 365-day climate sequences — temperature, precipitation, solar radiation.

🚜MLP

Management MLP Encoder

Deep MLP encodes planting density, nitrogen rates, irrigation regime, and tillage practices.

⚡Fusion

Trilinear Attention Fusion

Novel cross-modal fusion captures G×E, G×M, E×M, and G×E×M interaction tensors jointly.

📊UQ

Uncertainty Quantification

Monte Carlo dropout and conformal prediction yield calibrated 90% prediction intervals.

🔍XAI

SHAP Explainability

Feature attribution via SHAP values, temporal attention maps, and counterfactual management sweeps.

Benchmark Results

Evaluated on the G2F Genomes-to-Fields maize dataset (2014–2022, 12 environments).

Model	RMSE (t/ha)	R²	Pearson r	Parameters
G×E×M (Ours) SOTA	0.41	0.93	0.96	8.2M
GBLUP	0.89	0.74	0.86	—
DeepGS	0.71	0.81	0.90	2.1M
LFMM + Env	0.68	0.83	0.91	—
XGBoost + Features	0.63	0.85	0.92	—
EnvGWAS	0.77	0.79	0.89	—

Ready to explore?

Select a genotype, environment, and management strategy to get an instant yield prediction with uncertainty bounds.

Predict Crop Yield AcrossGenotype × Environment × Management