artificial intelligence in agriculture, smart agriculture, Agriculture 5.0, precision agriculture, agricultural artificial intelligence, machine learning in farming, deep learning in agriculture, crop monitoring, crop yield prediction, digital twin farming, remote sensing and GIS, soil health monitoring, livestock monitoring, agricultural robotics, blockchain in agriculture, agricultural IoT, federated learning, climate-smart agriculture, farm decision support systems, smart greenhouse systems
Smart Agriculture through Mathematical Modelling and Computational Approaches explores how mathematical models, artificial intelligence and computational technologies are transforming contemporary agriculture. The book covers the evolution from traditional farming to Agriculture 5.0 and examines applications of machine learning, deep learning, digital twins, remote sensing, GIS and data analytics in crop monitoring, yield prediction, soil-health assessment and resource optimization.
It also discusses livestock monitoring, agricultural robotics, blockchain, IoT-enabled sensors, federated learning, climate forecasting, smart greenhouses and farm decision-support systems. Mathematical and computational approaches are presented as essential tools for improving productivity, sustainability, resilience and evidence-based farm management. The book further addresses ethical concerns, data privacy, cybersecurity, socioeconomic impacts, government policies and emerging research directions. Bringing together multidisciplinary perspectives, it is a valuable reference for students, researchers, agricultural scientists, engineers, policymakers and professionals working in smart agriculture, mathematical modelling, artificial intelligence and computational agricultural sciences.
