Deep neural networks autonomously learn and utilize critical environmental conditions to capture the impacts of weather on spring wheat performance

• DNN-EP deep learning models accurately predicted weather impacts on crop performance. • Prioritized bulk-feature permutation probes important features learned by DNN-EP. • Integration of DNN-EP with the Finlay-Wilkson model predicts variety performance. Environmental conditions play a critical role in shaping crop performance. Enviromic prediction (EP) aims to develop models that learn the hidden relationships between environmental conditions and crop traits, enabling forecasts of performance under new conditions. Here, we explored applications of deep learning for enviromic prediction. Deep neural networks (DNN) were trained on over 100,000 environmental features to predict wheat yield and other important traits recorded from 322 environments spanning two decades across 20 locations. The trained DNN models demonstrated a high level of prediction accuracy in both cross-validation and forecast schemes. To gain insights into the inner workings of these “black box” DNNs, we devised a prioritized bulk-feature sensitivity analysis and revealed that the DNNs autonomously learned and utilized key environmental conditions associated with phenotypic variation. Furthermore, we showed that predictions from the deep neural network enviromic prediction (DNN-EP) models could be integrated with Finlay-Wilkinson regression models to forecast variety-level performance in new environments. Our findings highlight the potential of deep learning to develop interpretable artificial intelligence models that accurately predict crop performance, offering guidance towards optimizing agricultural practices under changing environmental conditions.