Review of machine learning and deep learning models in agriculture

Machine learning (ML) refers to the processes that enable computers think based on various methods. It can be also called domain which is a subset of Artificial Intelligence (AI). Deep (DL) has been promising, new and modern technique for data analysis in recent years. shown as improved version Neural Networks (ANN) one popular AI methods today. The population world increasing day by importance agriculture parallel. Because this, many researchers have focused this issue tried apply machine deep under name smart farm technologies both increase agricultural production solve some challenges agriculture. In study, it aimed give detailed information about these up-to-date studies. 77 articles algorithms field published IEEE Xplore, ScienceDirect, Web Science Scopus publication databases between 2016 2020 years were reviewed. classified five categories plant recognition, disease detection, weed pest soil mapping-drought index, yield forecast. They examined detail terms learning/deep architectures, sets, performance metrics (Accuracy, Precision, Recall, F-Score, R2, MAPE, RMSE, MAE), obtained experimental results. Based articles, most methods, used sets/types, chosen criteria, results among existing studies are presented. seen number AI-based applications related compared past sustainability productivity so promising.