harvest-net

genia-americas/harvest-net

harvest-net is a machine learning model from genia-americas. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.

Crop yield prediction model trained on agricultural data from Brazil, Argentina, and Mexico. Reference model for the HarvestYield-LAC benchmark.

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Updated May 29, 2026

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License: apache-2.0
Access: Public
Created: May 29, 2026
Updated: May 29, 2026
SHA: 2979e55c

Model card

From the Hugging Face model README

HarvestNet

Crop yield prediction model trained on agricultural data from Brazil, Argentina, and Mexico. Reference model for the HarvestYield-LAC benchmark.

Model Description

Models trained on U.S. Midwest data systematically underperform on Southern Hemisphere growing conditions, soil types, and crop varieties. HarvestNet is trained on regional data from CONAB, SIAP, INTA, and INMET/IDEAM climate records.

Architecture: Gradient boosted trees with neural feature extraction
Inputs: climate variables, soil type, crop variety, management practices, satellite-derived vegetation indices
Output: yield prediction in tonnes/hectare with confidence interval

Crops Covered

Soy (Brazil, Argentina), Corn (Brazil, Mexico, Argentina), Sugarcane (Brazil), Wheat (Argentina), Coffee (Brazil, Colombia)

Performance

Crop	Region	RMSE	MAPE	R2
Soy	Brazil	0.31	4.2%	0.87
Soy	Argentina	0.28	3.9%	0.89
Corn	Brazil	0.44	5.1%	0.83
Corn	Mexico	0.51	6.3%	0.79
Wheat	Argentina	0.38	4.7%	0.85

Usage

from genia.agri import HarvestNet

model = HarvestNet.from_pretrained("genia-americas/harvest-net-v1") prediction = model.predict( crop="soy", region="mato_grosso_br", climate=climate_data, soil=soil_data ) print(prediction.yield_estimate, prediction.confidence_interval)

Citation

@techreport{genia2025harvestnet, title={HarvestNet: Crop Yield Prediction for Latin America}, author={GENIA Americas Corporation}, institution={GENIA Americas / RaceFor.AI}, year={2025}, url={https://github.com/GENIA-Americas/multimodal-ai-americas} }