Harnessing Artificial Intelligence and Machine Learning for Sustainable Agriculture in Asia: Insights from a Bibliometric Review

The global food system faces critical challenges, including the impacts of climate change and the need to feed a growing population. This study investigates the latest advancements in Artificial Intelligence (AI) and Machine Learning (ML) to achieve sustainable agriculture. Through a Natural Language Processing (NLP)-powered bibliometric review of more than 10,000 Scopus-indexed research papers, we identify key applications of AI and ML in top use cases of sustainable agriculture—water, crop, and soil. The analysis highlights trends, methodologies, and outcomes, showcasing AI’s transformative potential to enhance agricultural quality and productivity while reducing environmental impacts. By aligning with the United Nations Sustainable Development Goals (SDGs), the findings serve as a guide for fostering innovation and resilience in the agricultural sector.