Chatbots in Agriculture: A Literature Review

Résumé fourni par la source

Chatbots that automate dialogue promise support, faster responses, and personalization across sectors from customer service to education and health. Recent reviews highlight gains in efficiency and service quality. At the same time, public reporting underscores a gap between enterprise enthusiasm and user satisfaction, pointing to the need for better orchestration of human–AI handoffs and more reliable, context-aware systems. In agriculture, conversational agents align with broader digital-transformation agendas. They can translate expert knowledge into timely, localized advice, complement extension services, and integrate data streams (weather, markets, curated agronomy) to support day-to-day decisions. Following the PRISMA 2020 framework, 53 studies published between 2019 and October 2025 were identified across Scopus, Web of Science, ACM Digital Library, and IEEE Xplore. The analysis shows that the field is dominated by prototype-level contributions, which account for approximately 70% of the reviewed studies, information-provisioning use cases (69.8%), text-based interaction (58.5%), and a strong geographic concentration in India (64%). Evidence of operational deployment, longitudinal use, farmer adoption, and measured agricultural impact remains limited, and none of the reviewed publications substantively addresses General Data Protection Regulation compliance. Overall, the findings indicate that agricultural chatbots are currently supported mainly by prototype-level and preliminary feasibility evidence rather than demonstrated field impact, highlighting the need for stronger field evaluation, inclusive user-centered design, transparent validation practices, and clearer attention to governance and regulatory compliance.

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