A CNN-RNN Framework for Crop Yield Prediction (2019-11-20T00:00:00.000000Z)

TL;DR

The proposed CNN-RNN model, along with other popular methods such as random forest, deep fully connected neural networks (DFNN), and LASSO, was used to forecast corn and soybean yield across the entire Corn Belt for years 2016, 2017, and 2018 using historical data.

Abstract

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 (CNNs) and recurrent neural networks (RNNs) 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% and 8% of their respective average yields, substantially outperforming all other methods that were tested. The CNN-RNN has 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.

Authors

S. Archontoulis

2 papers

Saeed Khaki

2 papers

Lizhi Wang

2 papers

TL;DR

Abstract

Authors

References56 items

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Con ﬂ ict of Interest: The authors declare that the research was conducted in the absence of any commercial or ﬁ nancial relationships that could be construed as a potential con ﬂ ict of interest

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Source Code

This Paper Is Included in the Proceedings of the 12th Usenix Symposium on Operating Systems Design and Implementation (osdi '16). Tensorflow: a System for Large-scale Machine Learning Tensorflow: a System for Large-scale Machine Learning

this model uses the CNN-RNN model to predict yield based on the weather data without using soil and management data. This model only captures the linear and nonlinear effects of weather data

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