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
Most implemented papers
A CNN-RNN Framework for Crop Yield Prediction
saeedkhaki92/CNN-RNN-Yield-Prediction
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Crop yield prediction is extremely challenging due to its dependence on multiple factors such as crop genotype, environmental factors, management practices, and their interactions.
Multiple Instance Choquet Integral Classifier Fusion and Regression for Remote Sensing Applications
In classifier (or regression) fusion the aim is to combine the outputs of several algorithms to boost overall performance.
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.
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.
Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data
JiaxuanYou/crop_yield_prediction
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AAAI 2017 2017
Agricultural monitoring, especially in developing countries, can help prevent famine and support humanitarian efforts.
Crop Yield Prediction Using Deep Neural Networks
saeedkhaki92/Yield-Prediction-DNN
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Crop yield is a highly complex trait determined by multiple factors such as genotype, environment, and their interactions.
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.
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.
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.
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.
