Examining energy use efficiency and conducting an environmental life cycle assessment through the application of artificial intelligence: A case study on the production of cumin and fennel

This study aims to examine the energy use efficiency and conduct an environmental life cycle assessment (LCA) of the production of cumin and fennel using artificial intelligence (AI). The energy consumption for fennel and cumin is 34814.81 MJ ha−1 and 26214.17 MJ ha−1, respectively. The energy ratio for cumin is 0.66, indicating a substantial energy consumption per unit of output energy. Specifically, it takes 22.36 MJ of energy to produce 1 kg of cumin. Both crops exhibit a negative net energy balance. The results of the LCA analysis revealed three key findings when comparing the environmental emissions of cumin and fennel. Firstly, in the context of cumin cultivation, the environmental emissions related to human health were found to be 0.15 DALY. Secondly, there were no significant variations in the environmental emissions of medicinal plants when considering ecosystems. Finally, fennel production exhibited 237.77 USD2013 environmental publications in the resource category. It is worth noting that a substantial portion of the emissions in all three categories of medicinal plants can be attributed to direct emissions from crop cultivation. Additionally, nitrogen plays a significant role in emissions after the direct emissions occur. In comparison to fennel, cultivating cumin is recommended as it helps decrease environmental emissions and minimizes harm to water, air, and soil. The ANN model achieved a determination coefficient greater than 0.93 for four factors related to cumin cultivation in test mode. Similarly, the determination coefficient for output energy in fennel cultivation was found to be 0.852. However, the results indicate that the ANFIS test model outperformed the ANN model in terms of performance and accuracy. The ANFIS model exhibited an average detection coefficient of 0.95 for cumin cultivation and 0.93 for fennel cultivation, highlighting its superior performance compared to the ANN model. The application of AI techniques provides valuable insights into the production of cumin and fennel, facilitating informed decision-making towards more sustainable practices in the spice industry.