AI-Enabled Precision Agriculture for Smallholder Farmers

The application of artificial intelligence (AI) to precision agriculture has become a revolutionary tool for improving the productivity, sustainability, and resiliency of smallholder agricultural systems, especially in developing and rural settings. This paper will discuss the use of AI tools, including machine learning-based crop disease detection, yield prediction models, smart irrigation, and decision-support systems, in the context of smallholder farming based on a systematic review of 50 peer-reviewed articles that were published by reputable journals indexed in Elsevier/ScienceDirect, Taylor and Francis, Wiley, SAGE, and Springer Nature. The review also provides consistent evidence that AI applications have the potential to positively transform agricultural productivity by diagnosing diseases earlier, better using inputs, and managing farms based on data, as well as enhancing environmental sustainability. Nonetheless, the adoption by the smallholder farmers is still disproportionate and highly contextual. There are perceptions and intent to adopt AI-based technologies that are behavioral and socio-economic in nature and are strongly influenced by perceptions and attitudes to AI systems, trust, digital literacy, access, and cost of data infrastructure, and institutional support. The literature also points to the existence of severe obstacles like low levels of connectivity, skills gaps, disjointed extension services, ethical and data-governance issues, and the inaccessibility of high-tech solutions and smallholder realities. Meanwhile, the uptake and impact can be greatly improved with the help of the enabling factors, such as human-centered design, advisory and extension services, facilitating policies, and inclusive innovation ecosystems. This study contributes to the holistic insight into AI-enabled precision agriculture among smallholders by incorporating both technical performance evidence and socio-economic as well as policy viewpoints. It offers a conceptual basis and a research focus for future empirical investigations, stating that future AI solutions to optimize the advantages of digital agriculture must be context-aware, equitable, and farmer-focused to make sure that the advantages of digital agriculture are widely distributed across rural communities.