mlsanityData to 14 Oct 2026

An Open and Large-Scale Dataset for Multi-Modal Climate Change-aware Crop Yield Predictions

arXiv 2406.06081 ↗First posted 10 Jun 2024cs.LG cs.CVcited by 11

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Abstract (arXiv)

Precise crop yield predictions are of national importance for ensuring food security and sustainable agricultural practices. While AI-for-science approaches have exhibited promising achievements in solving many scientific problems such as drug discovery, precipitation nowcasting, etc., the development of deep learning models for predicting crop yields is constantly hindered by the lack of an open and large-scale deep learning-ready dataset with multiple modalities to accommodate sufficient information. To remedy this, we introduce the CropNet dataset, the first terabyte-sized, publicly available, and multi-modal dataset specifically targeting climate change-aware crop yield predictions for the contiguous United States (U.S.) continent at the county level. Our CropNet dataset is composed of three modalities of data, i.e., Sentinel-2 Imagery, WRF-HRRR Computed Dataset, and USDA Crop Dataset, for over 2200 U.S. counties spanning 6 years (2017-2022), expected to facilitate researchers in developing versatile deep learning models for timely and precisely predicting crop yields at the county-level, by accounting for the effects of both short-term growing season weather variations and long-term climate change on crop yields. Besides, we develop the CropNet package, offering three types of APIs, for facilitating researchers in downloading the CropNet data on the fly over the time and region of interest, and flexibly building their deep learning models for accurate crop yield predictions. Extensive experiments have been conducted on our CropNet dataset via employing various types of deep learning solutions, with the results validating the general applicability and the efficacy of the CropNet dataset in climate change-aware crop yield predictions.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 23 of its 69 references that are in the map. First posted on arXiv 10 Jun 2024.

Builds on

23 of 69 references.

References · 23

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11 more
  1. 2022Open High-Resolution Satellite Imagery: The WorldStrat Dataset -- With Application to Super-Resolutioncited by 40
  2. 2019A CNN-RNN Framework for Crop Yield Predictioncited by 36
  3. 2023MMST-ViT: Climate Change-aware Crop Yield Prediction via Multi-Modal Spatial-Temporal Vision Transformercited by 29
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 6; 2026: 3.

11 papers.

  1. 2024A Comprehensive Study on Quantization Techniques for Large Language Modelscited by 36
  2. 2024Towards Robust Vision Transformer via Masked Adaptive Ensemblecited by 4
  3. 2025AgriPotential: A Novel Multi-Spectral and Multi-Temporal Remote Sensing Dataset for Agricultural Potentialscited by 2
  4. 2025VITA: Variational Pretraining of Transformers for Climate-Robust Crop Yield Forecastingcited by 1
  5. 2026YieldSAT: A Multimodal Benchmark Dataset for High-Resolution Crop Yield Predictioncited by 1
  6. 2025Do Protein Transformers Have Biological Intelligence?cited by 0
  7. 2025DFYP: A Dynamic Fusion Framework with Spectral Channel Attention and Adaptive Operator learning for Crop Yield Predictioncited by 0
  8. 2025Out-of-Distribution Generalization in Climate-Aware Yield Prediction with Earth Observation Datacited by 0
  9. 2025Quantizing Space and Time: Fusing Time Series and Images for Earth Observationcited by 0
  10. 2026M3R: Localized Rainfall Nowcasting with Meteorology-Informed MultiModal Attentioncited by 0
  11. 2026A Multimodal Dataset for Large Language Model Applications in the Energy Domaincited by 0