A CNN-RNN Framework for Crop Yield Prediction

by   Saeed Khaki, et al.

Crop yield prediction is extremely challenging due to its dependence on multiple factors such as crop genotype, environmental factors, management practices, and their interactions. This paper presents a deep learning framework using convolutional neural networks (CNN) and recurrent neural networks (RNN) for crop yield prediction based on environmental data and management practices. The proposed CNN-RNN model, along with other popular methods such as random forest (RF), deep fully-connected neural networks (DFNN), and LASSO, was used to forecast corn and soybean yield across the entire Corn Belt (including 13 states) in the United States for years 2016, 2017, and 2018 using historical data. The new model achieved a root-mean-square-error (RMSE) 9 substantially outperforming all other methods that were tested. The CNN-RNN have three salient features that make it a potentially useful method for other crop yield prediction studies. (1) The CNN-RNN model was designed to capture the time dependencies of environmental factors and the genetic improvement of seeds over time without having their genotype information. (2) The model demonstrated the capability to generalize the yield prediction to untested environments without significant drop in the prediction accuracy. (3) Coupled with the backpropagation method, the model could reveal the extent to which weather conditions, accuracy of weather predictions, soil conditions, and management practices were able to explain the variation in the crop yields.


page 1

page 7

page 12

page 16

page 17

page 21


Crop Yield Prediction Using Deep Neural Networks

Crop yield is a highly complex trait determined by multiple factors such...

Corn Yield Prediction with Ensemble CNN-DNN

We investigate the predictive performance of two novel CNN-DNN machine l...

Using Deep Learning to Predict Plant Growth and Yield in Greenhouse Environments

Effective plant growth and yield prediction is an essential task for gre...

Crop Yield Prediction Integrating Genotype and Weather Variables Using Deep Learning

Accurate prediction of crop yield supported by scientific and domain-rel...

A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Prediction

Climate change is posing new challenges to crop-related concerns includi...

Development of Crop Yield Estimation Model using Soil and Environmental Parameters

Crop yield is affected by various soil and environmental parameters and ...

A Bayesian Network approach to County-Level Corn Yield Prediction using historical data and expert knowledge

Crop yield forecasting is the methodology of predicting crop yields prio...

Please sign up or login with your details

Forgot password? Click here to reset