  Advancing models for accurate estimation of food production is essential for
policymaking and managing national plans of action for food security. This
research proposes two machine learning models for the prediction of food
production. The adaptive network-based fuzzy inference system (ANFIS) and
multilayer perceptron (MLP) methods are used to advance the prediction models.
In the present study, two variables of livestock production and agricultural
production were considered as the source of food production. Three variables
were used to evaluate livestock production, namely livestock yield, live
animals, and animal slaughtered, and two variables were used to assess
agricultural production, namely agricultural production yields and losses. Iran
was selected as the case study of the current study. Therefore, time-series
data related to livestock and agricultural productions in Iran from 1961 to
2017 have been collected from the FAOSTAT database. First, 70% of this data was
used to train ANFIS and MLP, and the remaining 30% of the data was used to test
the models. The results disclosed that the ANFIS model with Generalized
bell-shaped (Gbell) built-in membership functions has the lowest error level in
predicting food production. The findings of this study provide a suitable tool
for policymakers who can use this model and predict the future of food
production to provide a proper plan for the future of food security and food
supply for the next generations.
