Technological Tools in Industry 4.0 for the Continuity of Agricultural Processes: A Systematic Literature Review

Abstract

Agriculture faces challenges in ensuring the continuity of its processes due to the lack of advanced technological tools for Industry 4.0. The problem is that a large portion of agricultural areas still rely on traditional methods and lack intelligent systems that allow for real-time data analysis and decision optimization, which limits their productivity, sustainability, and adaptability to climate change. To address this problem, a Systematic Literature Review was conducted using PICO approach, analyzing 45 articles selected from a total of 154 found in the ScienceDirect database (2023–2025). The results show that tools such as Machine Learning, Geographic Information Systems, and Digital Twins make it possible to locate new productive fields, simulate agricultural environments, and optimize decisions. However, their integration into Precision Agriculture is still limited. Furthermore, benefits such as disease detection, predictive maintenance, and increased productivity were identified. The purpose of this paper is to propose the integration of tools such as Deep Learning and Federated Learning, combined with sensors and IoT, to achieve more autonomous, sustainable, and data-secure agriculture. It concludes that these technologies are essential for modernizing agriculture, especially in countries like Peru, where they can improve productivity, decision-making, and the resilience of the agricultural sector.