A CNN-RNN Framework for Crop Yield Prediction

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 (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% and 8% of their respective average yields, 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.

26 Pages, 14 Figures

References in corpus (3)

• Crop Yield Prediction Using Deep Neural Networks
• Maize Yield and Nitrate Loss Prediction with Machine Learning Algorithms
• Classification of Crop Tolerance to Heat and Drought: A Deep Convolutional Neural Networks Approach

Cited by in corpus (12)

• Coupling Machine Learning and Crop Modeling Improves Crop Yield Prediction in the US Corn Belt
• Forecasting Corn Yield with Machine Learning Ensembles
• Simultaneous Corn and Soybean Yield Prediction from Remote Sensing Data Using Deep Transfer Learning
• Winter wheat yield prediction using convolutional neural networks from environmental and phenological data
• The Role of Cross-Silo Federated Learning in Facilitating Data Sharing in the Agri-Food Sector
• Predicting Yield Performance of Parents in Plant Breeding: A Neural Collaborative Filtering Approach
• Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharing
• Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing Applications
• Classification of Crop Tolerance to Heat and Drought: A Deep Convolutional Neural Networks Approach
• FARM: Fine-Tuning Geospatial Foundation Models for Intra-Field Crop Yield Regression
• Non-Linear Model-Based Sequential Decision-Making in Agriculture
• Corn Yield Prediction with Ensemble CNN-DNN