Smart farming to transform agriculture

Agriculture has long been the backbone of the global economy but traditional farming methods are struggling to meet the demands of a growing population and mounting climate pressures. Today, artificial intelligence (AI) is revolutionising food production, making farming smarter, more efficient and economically more impactful than ever.
AI in agriculture, often referred to as "smart farming," employs advanced technologies such as machine learning, data analytics, robotics and Internet of Things (IoT) devices to optimise every stage of the farming process. These systems analyse data from soil sensors, weather forecasts, satellite imagery and drones to guide precise decisions on irrigation, fertilization and pest control. Such precision farming techniques have proven to significantly reduce costs and boost productivity. In some regions, AI tools are projected to increase crop yields by up to 30 per cent.
Recent global initiatives highlight the growing importance of AI in agriculture. For instance, a new AI Agriculture Ecosystem launched in Abu Dhabi aims to strengthen farmer resilience amid global food challenges by integrating cutting-edge technologies into traditional farming practices. In China's rice-growing regions, autonomous agricultural robots equipped with AI can now detect pests and forecast yields days in advance, enabling farmers to act proactively rather than reactively.
The economic impact of smart farming is substantial. Increased efficiency and higher yields translate into greater profitability for farmers while reducing input costs for water, fertilizers, and pesticides. At a national level, enhanced agricultural productivity strengthens food security, lowers import dependence and creates export opportunities, thereby bolstering rural economies and contributing to overall economic growth. AI-driven agritech startups and technology companies are also attracting investment and creating high-skilled jobs, fostering innovation ecosystems that extend beyond traditional agriculture.
However, challenges remain. High initial costs, limited digital infrastructure in remote regions, and the need for technical training can slow adoption. To bridge this gap, governments and private sectors must collaborate to provide affordable AI solutions, digital education, and supportive policies that help farmers transition smoothly into the era of smart farming.
Sohana Sabrin Bithi
Undergraduate Student
North South University (NSU)