Advancement and Challenges of Implementing Artificial Intelligence of Things in Precision Agriculture

Artificial Intelligence (AI) integration has ushered in a transformative era in agriculture known as Smart Agriculture. This chapter comprehensively explores the AI concept within the context of smart agriculture. It conducts a systematic literature review, revealing a growing trend in AI-related publications and highlighting its applications and advantages. The evolution of agriculture from traditional methods to the current Agriculture 4.0 era is outlined, showcasing the pivotal role of advanced technologies such as Wireless Sensor Networks (WSN), Internet of Things (IoT), AI, etc. The ongoing revolution enables intelligent agriculture systems with real-time decision-making capabilities, automation, and data-driven approaches, addressing critical challenges like climate change, water scarcity, and food security. A detailed examination of the technological integration in smart agriculture emphasizes the role of machine learning, deep learning, WSN, and robotics. These technologies facilitate data collection, analysis, decision-making, and the optimization of various aspects of farming, such as soil and water management, weed control, disease and pest management, crop prediction, harvesting, weather forecasting, and supply chain management. Despite the promising advancements, this chapter identifies challenges in smart agriculture, ranging from data quality issues and network connectivity challenges to storage and processing complexities. Furthermore, the chapter explores the cybersecurity challenges within the smart farming ecosystem, categorizing potential cyber-attacks into data attacks, networking and equipment attacks, supply chain attacks, compliance and regulation challenges, cyber terrorism, and cloud computing attacks. The discussion underscores the need for a holistic approach to cybersecurity involving stakeholders, adherence to best practices, and continuous innovation to ensure the sustainability and security of smart agriculture.