Spinal injuries

Artificial intelligence (AI) is increasingly positioned as a technology capable of relieving the structural pressures facing agricultural extension systems, including shortages of trained advisors, rising agronomic complexity, and climate variability (Aker, 2011; Neeraja et al., 2026). Organic farming, which depends on ecological knowledge, site-specific soil and pest management, and certification integrity rather than synthetic inputs, presents both distinct opportunities and distinct constraints for AI-enabled extension (Brumă et al., 2023). This review synthesises peer-reviewed literature published between 2011 and 2026 on AI applications for agricultural and organic-farming extension, organising the evidence around three themes: opportunities, challenges, and ethical concerns. The review finds that machine learning and deep learning models support soil health monitoring, pest and disease diagnostics, and predictive decision support relevant to organic systems (Morellos et al., 2016; Upadhyay et al., 2025), while conversational and multilingual advisory platforms are extending reach to smallholder and low-literacy populations (Li et al., 2022; Javaid et al., 2023). At the same time, the literature consistently documents a digital divide, weak data governance, high technology costs, and limited farmer trust as persistent barriers to equitable adoption (Gardezi et al., 2023; Klerkx et al., 2019). Ethical concerns center on data privacy and ownership, algorithmic bias, accountability, and the risk that AI-driven extension could deepen inequities between large agribusinesses and smallholder or organic producers (Dara et al., 2022; Saha et al., 2026). The review concludes that realising the benefits of AI for organic farming extension requires farmer-centred design, transparent governance, and hybrid human–AI advisory models.