Abstract
Graphical Abstract Abstract Recently, machine learning (ML) models have been widely applied to support food security using heterogeneous and complex data. Thus, several papers have been published in this context. The current manuscript exposes a systematic literature review to investigate various ML and Deep Learning (DL) models used in food security tasks (e.g. cropland mapping, crop type mapping, crop yield prediction and field delineation). This literature review identifies a clear end-to-end process of food security employing ML and DL models. Eventually, it summarizes the challenges of using ML and DL in food security analysis in complex and heterogeneous data, computational analysis, and evaluation challenges.