Crop Yield Prediction
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Benchmarks
2018 Syngenta (2016 val)
SICKLE
Most implemented
A CNN-RNN Framework for Crop Yield Prediction
Multiple Instance Choquet Integral Classifier Fusion and Regression for Remote Sensing Applications
The CropAndWeed Dataset: A Multi-Modal Learning Approach for Efficient Crop and Weed Manipulation
EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task
Generative weather for improved crop model simulations
Papers
PhenoYieldNet: Learning Crop-Aware Phenological Responses for Multi-Crop Yield Prediction
Accurate crop yield prediction is crucial for sustainable agriculture and global food security. While existing methods are predominantly developed for single-crop prediction, they often struggle to generalize across dive…
Crop Yield PredictionAttention-based Multi-modal Deep Learning Model of Spatio-temporal Crop Yield Prediction with Satellite, Soil and Climate Data
Crop yield prediction is one of the most important challenge, which is crucial to world food security and policy-making decisions. The conventional forecasting techniques are limited in their accuracy with reference to t…
Crop Yield PredictionYieldSAT: A Multimodal Benchmark Dataset for High-Resolution Crop Yield Prediction
Crop yield prediction requires substantial data to train scalable models. However, creating yield prediction datasets is constrained by high acquisition costs, heterogeneous data quality, and data privacy regulations. Co…
Crop Yield PredictionRetrieval-Augmented Multi-scale Framework for County-Level Crop Yield Prediction Across Large Regions
This paper proposes a new method for crop yield prediction, which is essential for developing management strategies, informing insurance assessments, and ensuring long-term food security. Although existing data-driven ap…
Crop Yield PredictionUniCrop: A Universal, Multi-Source Data Engineering Pipeline for Scalable Crop Yield Prediction
Accurate crop yield prediction relies on diverse data streams, including satellite, meteorological, soil, and topographic information. However, despite rapid advances in machine learning, existing approaches remain crop-…
Crop Yield PredictionHarvesting AlphaEarth: Benchmarking the Geospatial Foundation Model for Agricultural Downstream Tasks
Geospatial foundation models (GFMs) have emerged as a promising approach to overcoming the limitations in existing featurization methods. More recently, Google DeepMind has introduced AlphaEarth Foundation (AEF), a GFM p…
Crop Yield Prediction