Artificial Intelligence and Machine Learning for Enhancing Labour Productivity in Agrifood Systems

Agriculture remains one of the most labour-intensive sectors globally, yet many countries are experiencing declining agricultural labour availability due to urbanization, ageing rural populations, migration, and changing employment patterns. Simultaneously, agrifood systems are under increasing pressure to improve productivity while minimizing environmental impacts and adapting to climate change. Recent advances in AI and ML have enabled significant progress in crop monitoring, pest and disease diagnosis, precision irrigation, yield forecasting, robotic harvesting, livestock management, supply-chain optimization, and farm decision support. These technologies have the potential not only to substitute repetitive manual tasks but also to augment human decision-making, improve resource-use efficiency, and increase farm profitability. Despite rapid technological development, evidence regarding their effectiveness, scalability, economic viability, and implications for labour markets remains fragmented. An integrated understanding of these issues is essential for achieving productive, resilient, and sustainable agrifood systems.Agrifood systems worldwide are facing increasing labour shortages, rising labour costs, demographic shifts, climate variability, and growing demands for sustainable food production. At the same time, rapid advances in artificial intelligence (AI) and machine learning (ML) are transforming agricultural decision-making, automation, and resource management. AI-powered technologies—including computer vision, robotics, autonomous machinery, predictive analytics, and digital decision-support systems—offer unprecedented opportunities to enhance labour productivity while improving production efficiency, resilience, and environmental sustainability.This Research Topic aims to provide a comprehensive platform for interdisciplinary research on the role of AI and ML in enhancing labour productivity across agrifood systems. It seeks to bridge agronomy, agricultural engineering, data science, economics, and policy by showcasing innovations that improve labour efficiency, reduce drudgery, support precision management, and strengthen sustainable agricultural production. The collection also aims to identify barriers to adoption, evaluate socio-economic impacts, and propose pathways for responsible, inclusive, and scalable implementation of AI-enabled solutions in diverse farming systems.This Research Topic welcomes original research articles, review papers, mini reviews, perspectives, methods papers, case studies, and policy analyses that explore the contribution of AI and ML to labour productivity within agrifood systems. Topics of interest include, but are not limited to:• AI- and ML-based precision crop and livestock management• Agricultural robotics and autonomous farming systems• Computer vision for crop health monitoring, weed detection and harvesting• Intelligent irrigation, nutrient and pest management• AI-driven farm decision-support systems and digital advisory services• Labour productivity assessment using AI-enabled technologies• Human–AI collaboration and workforce transformation in agriculture• The socio-economic impacts of AI adoption on farmers, labour markets and rural livelihoods• Adoption barriers, digital inclusion and capacity development for smallholder agriculture• AI for climate-smart, sustainable and resilient agrifood systems• Ethical, governance and policy dimensions of AI deployment in agriculture• Generative AI, digital twins, the Internet of Things, remote sensing and unmanned aerial systems for improving agricultural productivity• Interdisciplinary research integrating agronomic science with economics, engineering, data analytics, environmental sustainability and public policy• Comparative studies from developed and developing countries Topic editors: A Amarender Reddy, Namrata Singha Roy Source: https://www.frontiersin.org/research-topics/83863/artificial-intelligence-and-machine-learning-for-enhancing-labour-productivity-in-agrifood-systems