Application of artificial intelligence in agri-tech, environmental and biodiversity conservation

New agricultural technologies have increased agricultural capacity and efficiency. Artificial intelligence-based methodologies have been widely adopted in agricultural systems, resulting in successes in smart crop management, smart plant breeding, smart livestock farming, precision aquaculture farming, and agricultural robotics. However, machine learning models are limited by their dependence on huge, expensive labelled datasets for training, specialized skills for development and maintenance, and task-specificity, limiting their generalizability. Nevertheless, sustainable agriculture solutions are essential as climate change and population growth increase. Smart agriculture, which uses technology and data analytics, can improve social, economic, and environmental sustainability. This paper presents a comprehensive investigation of smart technologies in agriculture, their impact on sustainability, architectural design, and major variables influencing their adoption based on recent research. We critically analyze smart agriculture to show how it might reduce environmental impact, boost economic growth, and promote social inclusivity. This research highlights the importance of artificial intelligence in agriculture and its domains. • Presents a detailed analysis of how AI technologies like machine learning, IoT, and robotics are transforming agriculture. • Emphasizes the potential of AI in promoting environmental sustainability by minimizing the ecological impact of agriculture. • Identifies several barriers to the adoption of AI in agriculture, including economic, social, and technical issues. • Future research directions outline the need for interdisciplinary approaches, improved training for farmers, and development of cost-effective AI solutions. • Importance of evolving regulatory frameworks to address data privacy, cybersecurity, and economic concerns is stressed.