Artificial Intelligence technology is being implemented in various industries, including healthcare, automotive, manufacturing, finance, and agriculture. It also supports these industries in overcoming traditional difficulties in increasing productivity and efficiency. Agricultural automation is a major source of concern and a hot topic around the world. The world's population is rapidly growing, and it comes with increased demand for food and work. The farmers' traditional practices are not sufficient to meet these objectives. In this paper it is examined how computer science is used in the farming and agricultural industries. An agricultural production system is being forced into a replacement paradigm, according to the Food and Agriculture Organization of the United Nations. Rapid population growth, shrinking farmland, depleting natural resources, erratic climate change, and shifting market demands are all contributing to this trend. The new agricultural system must be more productive in terms of production, more efficient in terms of operation, more resilient to global climate change, and more sustainable for future generations to be effective and sustainable. When it comes to processing data and generating patterns, deep learning is a synthetic intelligence function that mimics the human brain. Deep learning can be utilized in decision-making since it mimics how the brain works. Image processing and huge data analysis are among the advanced techniques that have tremendous potential in this field. Numerous applications of deep learning techniques are being explored in agriculture, including disease detection, fruit or plant classification, and agricultural management processes, among other things.