Advancing Agriculture Automation Systems: Technological Innovations, Possible Applications, Challenges, and Recommendations

Agricultural automation (AA) driven by artificial intelligence (AI) and the Internet of Things (IoT) represents a transformative approach to addressing modern farming challenges, such as resource optimization, animal health monitoring, precision farming, and supply chain efficiency. This study examines the adoption and development of AI and IoT technologies in agriculture over the past decade, focusing on key advancements, trends, and their practical applications in the field. A bibliometric analysis of 3404 publications from 2014 to 2024, revealing a 402% growth in research output over the decade, with 18.21% of contributions originating from China and 13.82% from the United States, highlighting these nations’ leadership in this field. Prominent themes include smart agriculture, precision farming, and AI‐driven decision‐making systems. The findings also show a comparatively lower contribution from European countries, indicating potential areas for collaborative growth. This analysis identifies critical tools and technologies, such as IoT‐enabled sensors and AI‐powered data analytics that address real‐time agricultural issues, such as crop health monitoring and yield prediction. The bibliometric analysis identifies key themes including smart agriculture, precision farming, and AI‐driven decision systems. Performance data from reviewed studies show that Long Short‐Term Memory (LSTM) models achieve up to 97% accuracy in yield prediction based on time‐series data, while convolutional neural networks reach 90%–99% accuracy in image‐based plant disease detection. IoT‐enabled precision irrigation systems demonstrate 20%–30% water savings, and autonomous machinery has been shown to reduce labor requirements by up to 25%. Furthermore, the study anticipates significant future advancements, including enhanced energy‐efficient IoT devices and integration of robotics in farming. By presenting a comprehensive review of the literature and identifying gaps in current research, this work provides valuable insights for policymakers, researchers, and industry stakeholders aiming to accelerate the adoption of AI and IoT in agriculture.