  Food production is a vital global concern and the potential for an agritech
revolution through artificial intelligence (AI) remains largely unexplored.
This paper presents a comprehensive review focused on the application of
machine learning (ML) in agriculture, aiming to explore its transformative
potential in farming practices and efficiency enhancement. To understand the
extent of research activity in this field, statistical data have been gathered,
revealing a substantial growth trend in recent years. This indicates that it
stands out as one of the most dynamic and vibrant research domains. By
introducing the concept of ML and delving into the realm of smart agriculture,
including Precision Agriculture, Smart Farming, Digital Agriculture, and
Agriculture 4.0, we investigate how AI can optimize crop output and minimize
environmental impact. We highlight the capacity of ML to analyze and classify
agricultural data, providing examples of improved productivity and
profitability on farms. Furthermore, we discuss prominent ML models and their
unique features that have shown promising results in agricultural applications.
Through a systematic review of the literature, this paper addresses the
existing literature gap on AI in agriculture and offers valuable information to
newcomers and researchers. By shedding light on unexplored areas within this
emerging field, our objective is to facilitate a deeper understanding of the
significant contributions and potential of AI in agriculture, ultimately
benefiting the research community.
