Key points are not available for this paper at this time.
The convergence of nanotechnology and artificial intelligence (AI) represents a transformative force in agricultural economics, offering innovative solutions to longstanding challenges such as productivity inefficiencies, environmental degradation, and unsustainable resource use. This study presents a systematic literature review (SLR) aimed at synthesising theoretical frameworks, applications, and economic implications associated with these technologies in agriculture. A structured search strategy was developed using Boolean operators to combine key terms related to nanotechnology, AI, and machine learning. Comprehensive searches were conducted across six academic databases—Springer, IEEE Xplore, ACM, Science Direct, Wiley, and Google Scholar—complemented by manual and snowballing techniques. From an initial pool of 840 records, 55 studies met the inclusion criteria after rigorous screening and eligibility assessment. Findings indicate that nanotechnology enhances nutrient delivery, pest control, and crop monitoring through nanosensors and nano-fertilisers, while AI facilitates data-driven decision-making, yield prediction, and resource optimisation in precision farming. Despite promising results, challenges such as high initial investment, technological complexity, and limited access for smallholder farmers remain significant. The review concludes that the integration of nanotechnology and AI can improve agricultural efficiency, economic viability, and environmental sustainability. However, targeted investments, capacity-building, and interdisciplinary collaboration are essential to bridge the gap between innovation and implementation in developing economies.
Chavula et al. (Wed,) studied this question.