SOTAVerified

Crop Yield Prediction

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–25 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 Samples—0
Multi-modal Data Fusion and Deep Ensemble Learning for Accurate Crop Yield Prediction—0
Integrating remote sensing data assimilation, deep learning and large language model for interactive wheat breeding yield prediction—0
CMAViT: Integrating Climate, Managment, and Remote Sensing Data for Crop Yield Estimation with Multimodel Vision TransformersCode0
Federated Learning Architectures: A Performance Evaluation with Crop Yield Prediction Application—0
Explainability of Sub-Field Level Crop Yield Prediction using Remote Sensing—0
Naïve Bayes and Random Forest for Crop Yield Prediction—0
Generative weather for improved crop model simulationsCode0
Adaptive Fusion of Multi-view Remote Sensing data for Optimal Sub-field Crop Yield PredictionCode0
Corn Yield Prediction Model with Deep Neural Networks for Smallholder Farmer Decision Support SystemCode0
Cotton Yield Prediction Using Random ForestCode0
Innovations in Agricultural Forecasting: A Multivariate Regression Study on Global Crop Yield Prediction—0
Revolutionizing Global Food Security: Empowering Resilience through Integrated AI Foundation Models and Data-Driven Solutions—0
A Hybrid Deep Learning-based Approach for Optimal Genotype by Environment Selection—0
Predicting Crop Yield With Machine Learning: An Extensive Analysis Of Input Modalities And Models On a Field and sub-field Level—0
Integrating processed-based models and machine learning for crop yield prediction—0
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All datasets2018 Syngenta (2016 val)SICKLE

Benchmark Results

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