Innovative applications of internet of things and machine learning in sustainable agricultural irrigation management: Benefits and challenges

Abstract

Challenges in the agricultural sector include those relating to climate, demographics, as well as managing water resources. Using the Internet of Things (IoT), as well as the concept of artificial intelligence (AI), particularly machine learning (ML), offer technological solutions to optimize agricultural irrigation. However, the adoption of these technologies in irrigation systems remains limited due to associated risks, system complexity, and technical constraints. This article analyzes smart irrigation by integrating IoT and ML technologies in optimizing crop water use in agriculture. Effective water management is now essential to address challenges such as urbanization, climate change, and food security. IoT- and ML-based solutions offer a promising way to support more efficient and sustainable agricultural practices. This is because IoT systems provide real-time information about environmental and soil conditions. Moreover, ML techniques provide tools that will ensure that this instantaneous information obtained is of much use in making appropriate decisions in carrying out the irrigation process of agricultural products. This work provides a systematic review of the existing literature using the Preferred Reporting Items for Systematic Reviews (PRISMA) methodology. From an initial set of 1,340 publications, 108 studies met the inclusion criteria and were analyzed in depth. These studies offer a comprehensive understanding of IoT- and ML-based smart irrigation systems, highlighting their benefits in improving water conservation, irrigation efficiency, and agricultural productivity