Intelligent decision support systems in smart agriculture: trends, applications, and challenges
Keywords:
smart agriculture, decision support systems, artificial intelligence, Internet of Things, precision agriculture.Abstract
Intelligent Decision Support Systems (DSS) have become one of the most significant technological advances in smart agriculture by integrating artificial intelligence, machine learning, the Internet of Things (IoT), Big Data, sensors, unmanned aerial vehicles (UAVs), and satellite imagery to optimise the management of agricultural production systems. These technologies enable the processing of large volumes of real-time data and generate recommendations that improve planning, crop monitoring, and farm management, promoting more efficient, sustainable, and resilient agricultural practices in response to climate change and global food security challenges. The aim of this scoping review was to analyse the current state of knowledge on intelligent decision support systems in smart agriculture by identifying the main technological trends, applications, benefits, and challenges reported in the scientific literature. The review was conducted following the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines through the analysis of scientific articles, review papers, and technical reports published between 2018 and 2024. The findings indicate that intelligent decision support systems significantly improve agricultural productivity by optimising the use of water, fertilisers, and other agricultural inputs, enhancing early detection of pests and crop diseases, strengthening yield prediction, and supporting data-driven decision-making. Nevertheless, important challenges remain, including system interoperability, data quality and availability, rural connectivity, user training, cybersecurity, and data governance. It is concluded that intelligent decision support systems represent a strategic component of the digital transformation of the agricultural sector. However, their successful implementation requires stronger technological infrastructure, inclusive public policies, and further research aimed at improving the integration, accessibility, transparency, and reliability of these technologies across diverse agricultural contexts.
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