# A Hybrid Deep Learning-based Approach for Optimal Genotype by Environment Selection

arXiv: https://arxiv.org/abs/2309.13021 · HTML: https://mlsanity.com/paper/2309.13021/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 52 references that are in the map.
- First posted on arXiv 22 Sep 2023.

## Builds on (most cited first, 11 of 11)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1808.03314/) (2018)
- [A Critical Review of Recurrent Neural Networks for Sequence Learning](https://mlsanity.com/paper/1506.00019/) (2015)
- [A CNN-RNN Framework for Crop Yield Prediction](https://mlsanity.com/paper/1911.09045/) (2019)
- [Crop Yield Prediction Using Deep Neural Networks](https://mlsanity.com/paper/1902.02860/) (2019)
- [Corn Yield Prediction with Ensemble CNN-DNN](https://mlsanity.com/paper/2105.14351/) (2021)
- [Crop Yield Prediction Integrating Genotype and Weather Variables Using Deep Learning](https://mlsanity.com/paper/2006.13847/) (2020)
- [Predicting Yield Performance of Parents in Plant Breeding: A Neural Collaborative Filtering Approach](https://mlsanity.com/paper/2001.09902/) (2020)
- [Winter wheat yield prediction using convolutional neural networks from environmental and phenological data](https://mlsanity.com/paper/2105.01282/) (2021)
- [Automated Agriculture Commodity Price Prediction System with Machine Learning Techniques](https://mlsanity.com/paper/2106.12747/) (2021)

## Abstract (arXiv)

Precise crop yield prediction is essential for improving agricultural practices and ensuring crop resilience in varying climates. Integrating weather data across the growing season, especially for different crop varieties, is crucial for understanding their adaptability in the face of climate change. In the MLCAS2021 Crop Yield Prediction Challenge, we utilized a dataset comprising 93,028 training records to forecast yields for 10,337 test records, covering 159 locations across 28 U.S. states and Canadian provinces over 13 years (2003-2015). This dataset included details on 5,838 distinct genotypes and daily weather data for a 214-day growing season, enabling comprehensive analysis. As one of the winning teams, we developed two novel convolutional neural network (CNN) architectures: the CNN-DNN model, combining CNN and fully-connected networks, and the CNN-LSTM-DNN model, with an added LSTM layer for weather variables. Leveraging the Generalized Ensemble Method (GEM), we determined optimal model weights, resulting in superior performance compared to baseline models. The GEM model achieved lower RMSE (5.55% to 39.88%), reduced MAE (5.34% to 43.76%), and higher correlation coefficients (1.1% to 10.79%) when evaluated on test data. We applied the CNN-DNN model to identify top-performing genotypes for various locations and weather conditions, aiding genotype selection based on weather variables. Our data-driven approach is valuable for scenarios with limited testing years. Additionally, a feature importance analysis using RMSE change highlighted the significance of location, MG, year, and genotype, along with the importance of weather variables MDNI and AP.