Artificial Intelligence and Big Data Analytics for Smart Farming and Agricultural Decision Support Systems
TL;DR
Smart farming systems collect large volumes of data from soil and weather sensors, satellites, drones, farm machinery, cameras, mobile applications, livestock devices, market platforms, and historical farm records to identify patterns that are difficult to detect through conventional observation.
Abstract
Artificial intelligence and big data analytics are transforming agriculture by enabling farmers to convert diverse information into accurate, timely, and actionable decisions. Smart farming systems collect large volumes of data from soil and weather sensors, satellites, drones, farm machinery, cameras, mobile applications, livestock devices, market platforms, and historical farm records. Artificial intelligence techniques—including machine learning, deep learning, computer vision, natural language processing, expert systems, and predictive analytics—process these datasets to identify patterns that are difficult to detect through conventional observation. Their applications include crop and variety selection, yield forecasting, disease identification, irrigation scheduling, nutrient management, weed detection, livestock monitoring, weather-risk assessment, price prediction, and supply-chain optimization. Agricultural decision support systems integrate these analytical outputs with agronomic knowledge and farmers’ preferences to provide recommendations through dashboards, mobile applications, alerts, and automated equipment. These technologies can improve productivity, resource-use efficiency, profitability, climate resilience, and environmental sustainability by reducing unnecessary water, fertilizer, pesticides, energy, and labour. However, adoption remains limited by inadequate rural connectivity, high technology costs, fragmented data, poor interoperability, insufficient digital skills, cybersecurity threats, uncertain data ownership, biased algorithms, and limited model explainability. Addressing these challenges requires affordable infrastructure, open standards, representative datasets, farmer-centred design, transparent governance, institutional collaboration, and strong extension support. When developed responsibly and adapted to local farming conditions, artificial intelligence and big data analytics can strengthen agricultural decision-making, reduce production risks, and contribute significantly to sustainable and knowledge-driven food systems .