Crop Yield Prediction
14 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
Latest papers
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.
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.
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.
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.
The CropAndWeed Dataset: A Multi-Modal Learning Approach for Efficient Crop and Weed Manipulation
Precision Agriculture and especially the application of automated weed intervention represents an increasingly essential research area, as sustainability and efficiency considerations are becoming more and more relevant.
A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Prediction
As far as we know, this is the first machine learning method that embeds geographical knowledge in crop yield prediction and predicts the crop yields at county level nationwide.
Multimodal Performers for Genomic Selection and Crop Yield Prediction
haakom/pay-attention-to-genomic-selection
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Smart Agricultural Technology 2021
We show that the performer-based models significantly outperform the traditional approaches, achieving an R score of 0. 820 and a root mean squared error of 69. 05, compared to 0. 807 and 71. 63, and 0. 076 and 149. 78 for the best traditional neural network and traditional Bayesian approach respectively.
Predicting crop yields with little ground truth: A simple statistical model for in-season forecasting
We present a fully automated model for in-season crop yield prediction, designed to work where there is a dearth of sub-national "ground truth" information.
EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task
We frame Earth surface forecasting as the task of predicting satellite imagery conditioned on future weather.
EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts
Here, we define high-resolution Earth surface forecasting as video prediction of satellite imagery conditional on mesoscale weather forecasts.
