A rapidly expanding world population and extreme climate change have made food production a crucial challenge in the twentyirst century. Therefore, improving crop production through agricultural management could be an effective solution for this challenge. However, due to the associated cost and time to perform field works, researchers widely rely on agricultural system modeling to examine the impacts of different crop management scenarios. However, due to the complexity of agricultural system modeling, their applications in producing practical knowledge for producers are limited. Concurrently, deep learning techniques have been recognized as a preferred method when dealing with large datasets. This study was performed in three phases. First, A deep learning network was utilized and trained by incorporating a large number of datasets produced by the Decision Support System for Agrotechnology Transfer (DSSAT) model. To the best of our knowledge, no research has been done in the literature on modeling a cropping system by deep learning. An model accuracy level of around 98\% was obtained, and it was 770 times faster than classical crop models DSSAT in calculating 900,000 different crop growth scenarios. However, The second phase of the study examined the robustness of the deep learning model under a wider range of environmental factors (e.g., different irrigation and climatological conditions) while a deep learning structure was desired compare to the first study. To optimize the deep learning structure, three variable reduction methods were used (Bayesian, Spearman, and Principal Component Analysis). The result of this study showed that a deep learning structure could be developed that has a similar accuracy level as the original model while the structural size was reduced up to 80 times. In the third phase of the study, three techniques (L1/L2 regularization, and neurons dropout) were used to address the overfitting problem in some deep learning models. The L2 regularization was identified as the most effective method that increased model generalization and reduced overfitting. The overall results from this study demonstrated the effectiveness of the proposed deep learning technique in replicating the yield results from crop modeling under different climatological and management conditions.