The importance of imputation for having highly performing prediction models is highlighted in this work. Three imputation techniques are tested against a non-imputation approach that discards records with any missing values; the complete-case analysis (CCA). The deep learning linear memory vector recurrent neural network-RNN (LIME) imputation model is tested along with two other nondeep learning models such as the linear function and Last Observation Carried Forward (LOCF). The simple LSTM deep learning (DL) prediction model is deployed to decide the best performing imputation model, the one resulting in the lowest price and yield prediction errors. Five performance evaluation measures are utilized; the mean absolute error (MAE), the root mean square error (RMSE), R2 correlation measure along with two aggregated measures summarizing these three measures to decide the overall prediction performance; the average aggregated measure (AGM) for each considered step ahead and the average of the AGM across all considered steps ahead (AAGM). Based on AGM, it is found that the LIME imputation model leads to the best prediction performance of the simple LSTM DL model across both applications of 5 weeks ahead strawberry price and yield predictions using weather; W2P and W2Y. Therefore, the LIME imputed file is reused to train two compound DL models, Convolutional Long Short-Term Memory RNN with attention (ATT-ConvLSTM) and ATT-CNN-LSTM along with their Voting Regressor ensemble (VR). The same models are retrained with files preprocessed with the non-imputation approach, CCA. It is found that the overall AAGM of the compound DL and ensemble prediction models across all the 1, 2, 3, and 4 weeks ahead price predictions confirm that using LIME highly improves the prediction performance of the ensemble and its compound DL components. The VR ensemble price prediction performance is improved by 72% and the ATTConvLSTM component is improved by 89% compared to their performances without...