Applications of machine learning and deep learning techniques in smart agriculture: A review

Abstract

Smart farming is a novel concept that makes agriculture more productive by employing up-to-date information technologies. The most recent developments in automation and artificial intelligence empower farmers better to monitor all procedures and exert accurate treatments determined by machines with great precision. Farmers, experts and data scientists keep on dealing with techniques that allow for optimizing the human labor required in farming. Machine Learning (ML) and Deep Learning (DL) networks that do not need human intervention while performing automatic feature extraction have a significant advantage over previous algorithms. ML and DL allow performing continuous decision-making based on data analysis. Nowadays these techniques have been applied in many applications of smart agriculture such as land cover identification, crop disease detection, weeds removal, and pest recognition. The focus of this study is to review the potential of using ML and DL techniques in agricultural applications and focus on how they are used for smart agriculture.