Implementation of Artificial Intelligence of Things in Smart Agriculture Systems for Environmental Monitoring, Irrigation Management, and Agricultural Data Security
Keywords:
Smart Agriculture, Artificial Intelligence of Things, Internet of Things, Smart Irrigation, Environmental Monitoring, Agricultural Data Security.Abstract
The development of Artificial Intelligence of Things (AIoT) technology has driven the transformation of the agricultural sector towards a smarter, more efficient, and sustainable Smart Agriculture system. AIoT integrates artificial intelligence, the Internet of Things (IoT), wireless sensor networks, and data communication technology to support environmental monitoring, irrigation management, and agricultural data security. This study aims to analyze the implementation of AIoT in a Smart Agriculture system that focuses on environmental monitoring, intelligent irrigation management, and agricultural data security. The research method used is a literature review by analyzing various national and international journals that discuss AIoT, IoT, Wireless Sensor Network (WSN), smart irrigation, and cybersecurity in the agricultural sector. The results of the study show that the application of AIoT can increase the effectiveness of environmental monitoring through real-time monitoring of temperature, air humidity, soil moisture, and other agricultural parameters. AIoT-based irrigation systems have also been proven to optimize water use through automated decision-making based on data obtained from sensors. In addition, communication technologies such as LoRa, ZigBee, BLE, and mesh networks play an important role in increasing the reliability of data transmission in large agricultural areas. This study also shows that implementing data security mechanisms is necessary to protect agricultural systems from the threats of unauthorized access, data manipulation, and cyberattacks. Thus, AIoT plays a crucial role in improving productivity, resource efficiency, and the sustainability of modern agricultural systems. Future research can focus on developing more adaptive AI models, improving device interoperability, and strengthening cybersecurity in the Smart Agriculture ecosystem.
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