The values, challenges, and strategies of AI in empowering sustainable livelihoods for farmers

Abstract

Smart agriculture has gained significant global attention in recent years, capturing considerable attention from the academic community. However, existing research has mainly focused on the values of artificial intelligence (AI) in rural society and agricultural systems, ignoring its deep impact on farmers’ livelihoods. This oversight risks marginalizing farmers, a crucial subject of global agriculture and food systems, in the context of AI development. This study offers a literature review of the values, challenges, and strategies of AI in empowering farmers’ sustainable livelihoods through the sustainable livelihoods framework (SLF), explores how AI, as an external technical factor, influences the internal system’s resilience. Analyzing 129 studies from a narrative review, our findings indicate that AI can promote farmers’ natural resource management capabilities, including land, water, and climate, facilitate precise agricultural decision-making, strengthen rural social networks, modernize production tools, and improve access to microcredit. AI enhances farmers’ natural, human, social, physical, and financial capital resilience. However, AI in empowering farmers faces several challenges: the limited adaptability of AI in complex agricultural environments, the widening digital divide, the monopoly of AI platforms, the high costs, and rural financial limitations. We advocate for designing targeted and adapted AI agricultural tools, enhancing farmers’ AI literacy, building a multi-stakeholder collaboration network, improving the accessibility and universality of AI in agriculture, and strengthening the rural credit system. This study innovatively integrates the SLF with AI, breaking through existing research that only focuses on the value of AI for agricultural production. It comprehensively summarizes the values, challenges, and strategies of AI for farmers’ sustainable livelihoods, proposes a new farmer-centered perspective for AI agricultural research, constructs a systematic governance framework for AI in agriculture, and provides systematic policy insights for promoting inclusive and sustainable development of smart agriculture.