Prospects of Deep Learning and Edge Intelligence in Agriculture

Abstract

Agriculture is one of the high labor occupations around the globe. To meet the population growth and its demand, with the increase in labor cost, there is a need to explore efficient autonomous systems which may replace the traditional methods. Computer vision, edge, and deep learning (DL) models have become a promising area of research. This new paradigm of deep edge intelligence is most appropriate for agriculture activities where real-time decision-making is very important. In this chapter, the authors conduct a systematic literature review on deep learning-aided edge intelligence (EI) applications in agriculture to gather the evidence for prospects of DL at edge in agriculture. They discuss how DL models have shown outstanding performance within limited time and computation resources, and also provide future research directions to enhance the viability and applicability of complex deep learning (DL) models deployed at edge devices in agricultural applications.