ABSTRACT
Smart agriculture is emerging at a significant rate in recent times. It is a part of the information and communication technologies (ICT) movement that is ushering in agriculture in many ways, referred to as the Third Green Revolution. By enriching the crop yield process, smart farming enhances its manufacturing of high-quality food. This article focuses on crop cultivation techniques as well as applies deep learning (DL) and machine learning (ML) algorithms so as to offer responses to a number of challenges that emerge all through the cultivation process. Machine learning is a leading-edge technological innovation that assists farmers in reducing crop losses by providing specific crop recommendations and keen insight. Soil and water management, crop cultivation, crop disease detection, weed control, crop distribution, robust fruit counting and yield prediction are all examples of smart agricultural applications that employ deep learning. Farmers have experienced lots of new challenges recently, along with crop failure due to scarcity of rain, soil fertility issues, etc. As a result of the changing environment, this proposed study will assist by finding the most effective way to handle crops and harvest them.
