# Crop Yield Prediction Using Deep Neural Networks

arXiv: https://arxiv.org/abs/1902.02860 · HTML: https://mlsanity.com/paper/1902.02860/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2019.

## Built on by (most cited first, 29 of 29)

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- [A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Prediction](https://mlsanity.com/paper/2111.08900/) (2021)
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- [WheatNet: A Lightweight Convolutional Neural Network for High-throughput Image-based Wheat Head Detection and Counting](https://mlsanity.com/paper/2103.09408/) (2021)
- [Extreme Gradient Boosting for Yield Estimation compared with Deep Learning Approaches](https://mlsanity.com/paper/2208.12633/) (2022)
- [On the Generalizability of Foundation Models for Crop Type Mapping](https://mlsanity.com/paper/2409.09451/) (2024)
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- [WeedSense: Multi-Task Learning for Weed Segmentation, Height Estimation, and Growth Stage Classification](https://mlsanity.com/paper/2508.14486/) (2025)
- [Winter wheat yield prediction using convolutional neural networks from environmental and phenological data](https://mlsanity.com/paper/2105.01282/) (2021)
- [Densely connected neural networks for nonlinear regression](https://mlsanity.com/paper/2108.00864/) (2021)
- [Agri-GNN: A Novel Genotypic-Topological Graph Neural Network Framework Built on GraphSAGE for Optimized Yield Prediction](https://mlsanity.com/paper/2310.13037/) (2023)
- [Towards Global Crop Maps with Transfer Learning](https://mlsanity.com/paper/2211.04755/) (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)
- [Weather-Driven Agricultural Decision-Making Using Digital Twins Under Imperfect Conditions](https://mlsanity.com/paper/2508.08326/) (2025)
- [Advanced techniques and applications of LiDAR Place Recognition in Agricultural Environments: A Comprehensive Survey](https://mlsanity.com/paper/2601.22198/) (2026)
- [SEAGAN: domain-Specific and Edge-Aware Graph Attention Network for Dynamic Plant Processes](https://mlsanity.com/paper/2606.19623/) (2026)
- [Pixel-Precise Explainable Stress Indexing: A Semantic Segmentation Framework for Disease Severity Quantification in Field Crops](https://mlsanity.com/paper/2607.06585/) (2026)
- [A Hybrid Deep Learning-based Approach for Optimal Genotype by Environment Selection](https://mlsanity.com/paper/2309.13021/) (2023)
- [Challenges for Predictive Modeling with Neural Network Techniques using Error-Prone Dietary Intake Data](https://mlsanity.com/paper/2311.09338/) (2023)
- [Learning county from pixels: corn yield prediction with attention-weighted multiple instance learning](https://mlsanity.com/paper/2312.01001/) (2023)
- [Innovations in Agricultural Forecasting: A Multivariate Regression Study on Global Crop Yield Prediction](https://mlsanity.com/paper/2312.02254/) (2023)
- [A Machine Learning Approach for Crop Yield and Disease Prediction Integrating Soil Nutrition and Weather Factors](https://mlsanity.com/paper/2403.19273/) (2024)
- [Climate-Driven Doubling of U.S. Maize Loss Probability: Interactive Simulation with Neural Network Monte Carlo](https://mlsanity.com/paper/2408.02217/) (2024)
- [DFYP: A Dynamic Fusion Framework with Spectral Channel Attention and Adaptive Operator learning for Crop Yield Prediction](https://mlsanity.com/paper/2507.05849/) (2025)
- [Attention-based Multi-modal Deep Learning Model of Spatio-temporal Crop Yield Prediction with Satellite, Soil and Climate Data](https://mlsanity.com/paper/2604.19217/) (2026)
- [MixINN: Accelerating Plant Breeding by Combining Mixed Models and Deep Learning for Interaction Prediction](https://mlsanity.com/paper/2605.04744/) (2026)
- [Recent Advances in Agentic Agri-Robotic Phenotyping: A Perspective Review from Fragmented Multimodal Sensing to Unified PhenoAgent Intelligence](https://mlsanity.com/paper/2609.34567/) (2026)

## Abstract (arXiv)

Crop yield is a highly complex trait determined by multiple factors such as genotype, environment, and their interactions. Accurate yield prediction requires fundamental understanding of the functional relationship between yield and these interactive factors, and to reveal such relationship requires both comprehensive datasets and powerful algorithms. In the 2018 Syngenta Crop Challenge, Syngenta released several large datasets that recorded the genotype and yield performances of 2,267 maize hybrids planted in 2,247 locations between 2008 and 2016 and asked participants to predict the yield performance in 2017. As one of the winning teams, we designed a deep neural network (DNN) approach that took advantage of state-of-the-art modeling and solution techniques. Our model was found to have a superior prediction accuracy, with a root-mean-square-error (RMSE) being 12% of the average yield and 50% of the standard deviation for the validation dataset using predicted weather data. With perfect weather data, the RMSE would be reduced to 11% of the average yield and 46% of the standard deviation. We also performed feature selection based on the trained DNN model, which successfully decreased the dimension of the input space without significant drop in the prediction accuracy. Our computational results suggested that this model significantly outperformed other popular methods such as Lasso, shallow neural networks (SNN), and regression tree (RT). The results also revealed that environmental factors had a greater effect on the crop yield than genotype.