SOTAVerified

Crop Yield Prediction

Papers

Showing 110 of 48 papers

TitleStatusHype
DiffusionSat: A Generative Foundation Model for Satellite ImageryCode2
A Sensor Agnostic Domain Generalization Framework for Leveraging Geospatial Foundation Models: Enhancing Semantic Segmentation viaSynergistic Pseudo-Labeling and Generative LearningCode1
SICKLE: A Multi-Sensor Satellite Imagery Dataset Annotated with Multiple Key Cropping ParametersCode1
MMST-ViT: Climate Change-aware Crop Yield Prediction via Multi-Modal Spatial-Temporal Vision TransformerCode1
The CropAndWeed Dataset: A Multi-Modal Learning Approach for Efficient Crop and Weed ManipulationCode1
A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield PredictionCode1
Predicting crop yields with little ground truth: A simple statistical model for in-season forecastingCode1
EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction taskCode1
EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impactsCode1
MT-CYP-Net: Multi-Task Network for Pixel-Level Crop Yield Prediction Under Very Few Samples0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TransformerRMSE9.28Unverified
#ModelMetricClaimedVerifiedStatus
1U-TAEMAPE (%)49.63Unverified