The Impact of the Application of Deep Learning Techniques with IoT in Smart Agriculture

Abstract

Agriculture is a vital sector in Tunisia, serving as the country’s economic backbone and providing subsistence and employment to a large number of people. However, climate change, pesticide use, water scarcity, and other factors have resulted in decreased agricultural productivity and quality. In this work, we investigate the potential of deep learning techniques in combination with IoT systems to address these challenges and enhance agricultural practices. Specifically, we conduct an in-depth review of recent research on the application of deep learning algorithms with IoT devices in various agricultural applications. According to our results, this integration has the potential to significantly improve crop yields, reduce waste, and promote more sustainable farming practices. In addition, we highlight the synergistic relationship between IoT and DL in smart agriculture. Overall, this study provides valuable insights for farmers, researchers, and policymakers on how to employ DL and IoT to increase agricultural productivity and sustainability in Tunisia as well as globally.