Impact of Advanced Agriculture Technologies and Energy Consumption on Crop Yields in Modern Agriculture Using Deep Learning Techniques

Growing concern over food security has drawn worldwide scholarly attention. Addressing food security issues highlights the vulnerability of agricultural yield to the complexity of agriculture inputs. Therefore, this study considers the intricacies of cultivation inputs and their effect on cereal production across 20 developing Asian countries from 1990 to 2022. First, advanced machine learning algorithms are employed to investigate the combined impact of the farming Product Complexity Index on agricultural yields. Second, the Granger causality test was used to uncover the causality direction between agricultural yield and exogenous variables. Both the causal inference neural network (CINN) and deep neural network (DNN) models show a rapid initial decrease in loss during the early epochs, followed by a more gradual decline, indicating effective learning and convergence. Notably, the CINN model consistently starts with a lower loss compared to the DNN model, suggesting superior performance in minimizing the training loss. These machine learning techniques have successfully predicted the synergistic relationships, leading to significant improvements in cereal yield forecasting. The Granger causality results revealed feedback causality between the agricultural Product Complexity Index and crop yields and the use of fertilizer and agricultural yields on different lags. These results emphasize the potential for targeted guidelines that harness the interactions between complexities in agriculture and the application of fertilizer to improve cereal yields.