# A CNN-RNN Framework for Crop Yield Prediction

arXiv: https://arxiv.org/abs/1911.09045 · HTML: https://mlsanity.com/paper/1911.09045/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2019.

## Built on by (most cited first, 30 of 36)

- [MMST-ViT: Climate Change-aware Crop Yield Prediction via Multi-Modal Spatial-Temporal Vision Transformer](https://mlsanity.com/paper/2309.09067/) (2023)
- [A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Prediction](https://mlsanity.com/paper/2111.08900/) (2021)
- [The Role of Cross-Silo Federated Learning in Facilitating Data Sharing in the Agri-Food Sector](https://mlsanity.com/paper/2104.07468/) (2021)
- [Corn Yield Prediction with Ensemble CNN-DNN](https://mlsanity.com/paper/2105.14351/) (2021)
- [An Open and Large-Scale Dataset for Multi-Modal Climate Change-aware Crop Yield Predictions](https://mlsanity.com/paper/2406.06081/) (2024)
- [WheatNet: A Lightweight Convolutional Neural Network for High-throughput Image-based Wheat Head Detection and Counting](https://mlsanity.com/paper/2103.09408/) (2021)
- [Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs](https://mlsanity.com/paper/2210.13648/) (2022)
- [Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharing](https://mlsanity.com/paper/2304.09876/) (2023)
- [Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing Applications](https://mlsanity.com/paper/2301.01200/) (2022)
- [Winter wheat yield prediction using convolutional neural networks from environmental and phenological data](https://mlsanity.com/paper/2105.01282/) (2021)
- [A weakly supervised framework for high-resolution crop yield forecasts](https://mlsanity.com/paper/2205.09016/) (2022)
- [Corn Yield Prediction based on Remotely Sensed Variables Using Variational Autoencoder and Multiple Instance Regression](https://mlsanity.com/paper/2211.13286/) (2022)
- [AgriPotential: A Novel Multi-Spectral and Multi-Temporal Remote Sensing Dataset for Agricultural Potentials](https://mlsanity.com/paper/2506.11740/) (2025)
- [Domain-guided Machine Learning for Remotely Sensed In-Season Crop Growth Estimation](https://mlsanity.com/paper/2106.13323/) (2021)
- [Recent applications of machine learning, remote sensing, and iot approaches in yield prediction: a critical review](https://mlsanity.com/paper/2306.04566/) (2023)
- [TomatoDIFF: On-plant Tomato Segmentation with Denoising Diffusion Models](https://mlsanity.com/paper/2307.01064/) (2023)
- [A Comprehensive Review of AI-enabled Unmanned Aerial Vehicle: Trends, Vision , and Challenges](https://mlsanity.com/paper/2310.16360/) (2023)
- [WeatherFormer: A Pretrained Encoder Model for Learning Robust Weather Representations from Small Datasets](https://mlsanity.com/paper/2405.17455/) (2024)
- [LSTM Autoencoder-based Deep Neural Networks for Barley Genotype-to-Phenotype Prediction](https://mlsanity.com/paper/2407.16709/) (2024)
- [VITA: Variational Pretraining of Transformers for Climate-Robust Crop Yield Forecasting](https://mlsanity.com/paper/2508.03589/) (2025)
- [FARM: Fine-Tuning Geospatial Foundation Models for Intra-Field Crop Yield Regression](https://mlsanity.com/paper/2510.26609/) (2025)
- [AgroFlux: A Spatial-Temporal Benchmark for Carbon and Nitrogen Flux Prediction in Agricultural Ecosystems](https://mlsanity.com/paper/2602.01614/) (2026)
- [Temporal Convolution Domain Adaptation Learning for Crops Growth Prediction](https://mlsanity.com/paper/2202.12120/) (2022)
- [Unsupervised Graph Spectral Feature Denoising for Crop Yield Prediction](https://mlsanity.com/paper/2208.02714/) (2022)
- [A Hybrid Deep Learning-based Approach for Optimal Genotype by Environment Selection](https://mlsanity.com/paper/2309.13021/) (2023)
- [Learning county from pixels: corn yield prediction with attention-weighted multiple instance learning](https://mlsanity.com/paper/2312.01001/) (2023)
- [Corn Yield Prediction Model with Deep Neural Networks for Smallholder Farmer Decision Support System](https://mlsanity.com/paper/2401.03768/) (2024)
- [Feasibility of machine learning-based rice yield prediction in India at the district level using climate reanalysis data](https://mlsanity.com/paper/2403.07967/) (2024)
- [Enhancing Strawberry Yield Forecasting with Backcasted IoT Sensor Data and Machine Learning](https://mlsanity.com/paper/2504.18451/) (2025)
- [DFYP: A Dynamic Fusion Framework with Spectral Channel Attention and Adaptive Operator learning for Crop Yield Prediction](https://mlsanity.com/paper/2507.05849/) (2025)

## Abstract (arXiv)

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.