Abstract
Deep learning, robotics, AI, and automation have lots of applications that are beneficial to society at large. In fact, nearly every sector, such as transportation, industries, manufacturing, healthcare, education, retail, and home automation, are adopting AI, machine learning, IoT, and robotics to their advantage. Of course, agriculture is no exception. The chapter starts with an introduction to the applications of deep learning in agriculture. Next, a comprehensive survey of the research work done in recent years is provided. It is followed by the description of various techniques of deep learning (DL). The next section briefly describes the traditional ways of weed detection and removal. Next, the architecture of deep learning for weed detection and removal is presented along with the associated code. Further, the chapter goes on to discuss the pros and cons of this approach. Finally, the chapter concludes by citing the important points discussed in this study.
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