Innovations in agriculture-smart solutions for sustainable food production

Abstract

The global population is projected to approach 9 billion by 2050, rising from 8.1 billion in 2024, which necessitates a 70% increase in agricultural output amid shrinking arable land. While agriculture continues to underpin global economies, fuelling GDP, employment, and international trade-traditional methods alone cannot suffice to meet future demand. Against this backdrop, artificial intelligence (AI) emerges as a critical enabler of next-generation agriculture. The application of AI in farming began with McKinion and Lemmon’s 1985 GOSSYM model for cotton optimization, and it has since expanded into a sophisticated ecosystem comprising drones, sensors, robotics, satellite imaging, IoT networks, and smartphone apps. These technologies facilitate precision agriculture, enabling detailed crop surveillance, variable‑rate irrigation and fertilization, remote sensing, soil and pest diagnostics, and climate-aware decision‑support-enhancing yields while reducing input waste. Robotic systems now perform field operations such as weeding, harvesting, and monitoring autonomously, mitigating labour shortages and human error. Meanwhile, IoT-based frameworks collect continuous data on soil moisture, temperature, pH, and nutrient status; machine learning algorithms then analyse this to guide crop recommendations and yield predictions with high accuracy. Despite these advancements, significant challenges persist, including uneven mechanization, data privacy and security, algorithmic flexibility in dealing with diverse crop conditions, and the high costs and complexity of managing vast, heterogeneous datasets. Addressing these issues is essential for achieving equitable and scalable adoption. This review synthesizes progress in soil management, weed control, and IoT integration. It surveys the development of agricultural robotics, offering case studies, identifying major obstacles, and outlining future prospects across different national contexts.