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 (CNNs) and recurrent neural networks (RNNs) 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 has 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.

BackgroundAbscisic acid (ABA) plays crucial roles in regulating plant growth and development, especially in responding to abiotic stress. The pyrabactin resistance-like (PYL) abscisic acid receptor family has been identified and widely characterized in Arabidopsis. However, PYL families in rice were largely unknown. In the present study, 10 out of 13 PYL orthologs in rice (OsPYL) were isolated and investigated.ResultsQuantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis showed that expression of OsPYL genes is tissue-specific and display differential response to ABA treatment, implying their functional diversity. The interaction between 10 OsPYL members and 5 protein phosphatase 2C in rice (OsPP2C) members was investigated in yeast two-hybrid and tobacco transient expression assays, and an overall interaction map was generated, which was suggestive of the diversity and complexity of ABA-sensing signaling in rice. To study the biological function of OsPYLs, two OsPYL genes (OsPYL3 and OsPYL9) were overexpressed in rice. Phenotypic analysis of OsPYL3 and OsPYL9 transgenic rice showed that OsPYLs positively regulated the ABA response during the seed germination. More importantly, the overexpression of OsPYL3 and OsPYL9 substantially improved drought and cold stress tolerance in rice.ConclusionTaken together, we comprehensively uncovered the properties of OsPYLs, which may be good candidates for the improvement of abiotic stress tolerance in rice.Electronic supplementary materialThe online version of this article (doi:10.1186/s12284-015-0061-6) contains supplementary material, which is available to authorized users.

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