Crop Yield Prediction
15 papers with code • 2 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Crop Yield Prediction
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CropAndWeed Dataset
SICKLE
Most implemented papers
Counterfactual Explanations of Neural Network-Generated Response Curves
GiorgioMorales/ResponsivityAnalysis
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We propose to use counterfactual explanations (CFEs) for the identification of the features with the highest relevance on the shape of response curves generated by neural network black boxes.
MMST-ViT: Climate Change-aware Crop Yield Prediction via Multi-Modal Spatial-Temporal Vision Transformer
fudong03/mmst-vit
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ICCV 2023
In this work, we develop a deep learning-based solution, namely Multi-Modal Spatial-Temporal Vision Transformer (MMST-ViT), for predicting crop yields at the county level across the United States, by considering the effects of short-term meteorological variations during the growing season and the long-term climate change on crops.
SICKLE: A Multi-Sensor Satellite Imagery Dataset Annotated with Multiple Key Cropping Parameters
Out of the 2, 370 samples, 351 paddy samples from 145 plots are annotated with multiple crop parameters; such as the variety of paddy, its growing season and productivity in terms of per-acre yields.
Cotton Yield Prediction Using Random Forest
From the 1980s to the 1990s, field data were gathered across the southern cotton belt of the United States.
Generative weather for improved crop model simulations
Accurate and precise crop yield prediction is invaluable for decision making at both farm levels and regional levels.
