Sustainable Systems Engineering Using Life Cycle Assessment: Application of Artificial Intelligence for Predicting Agro-Environmental Footprint

The increase in population has increased the need for agricultural and food products, thus production should be increased. This goal may cause increases emissions environmental impacts by increasing consumption of inputs. prediction plays an important role evaluating pollutant crop production. study employed two artificial intelligence (AI) methods: adaptive neuro-fuzzy inference system–fuzzy c-means (ANFIS–FCM) algorithm as a novel computational method, neural network (ANN) conventional method to predict soybean different scenarios (i.e., cultivation after rapeseed (R-S), wheat (W-S), fallow (F-S)). life cycle was assessed terms through IMPACT2002+ SimaPro. In present study, one ton soybeans considered functional unit, boundary system gate field. According results, each defined resulted 0.0009 0.0016 DALY, 5476.18 8799.80 MJ primary, 1033.68 1840.70 PDF × m2 yr, 563.55 880.61 kg CO2-eq damage human health, resources, ecosystem quality, climate change, respectively. Moreover, weighted analysis indicated that various led 293.87–503.73 mPt environment, which R-S scenario had best performance. ANFIS–FCM acted model indicators all cases related ANN. range calculated R2 ANFIS-FCM ANN models were between 0.9967 0.9989 0.9269 0.9870, It can concluded proposed is efficient technique obtaining accurate parameters cultivation.