Zhinong AI platform combines NLP, image diagnosis, and workflow orchestration for smallholder farmers

Zhinong AI is a comprehensive decision-support platform specifically designed for smallholder farmers. It brings together multiple AI capabilities: natural-language question answering allows farmers to ask agricultural queries in their own language; image-based crop disease diagnosis enables instant identification of plant health issues; plot management and a farming calendar help track field activities; and workflow orchestration guides farmers through complex agricultural tasks. Additionally, the platform integrates a Hainan Free Trade Port agricultural service zone and an age-friendly care mode to make the system accessible to older farmers. The system architecture follows a closed-loop decision process consisting of five stages: sensing (data collection), analysis (AI interpretation), planning (recommendation generation), execution (task guidance), and feedback (outcome evaluation and learning).

However, the authors note that this study is a design-science case study, not an empirical field evaluation. It constructs a layered system architecture, a function-pain-point mapping matrix, an evaluation indicator system, and a governance framework covering data provenance, model risk, expert review, privacy, and adoption risk. The key contribution is a structured research framework for transforming an AI agricultural prototype into an empirically testable, accountable, and localized decision-support infrastructure. This is critical because many AI agricultural tools fail in real-world adoption due to lack of integration, usability, and accountability. Zhinong AI aims to bridge that gap by providing a blueprint for building AI systems that smallholder farmers can actually trust and use effectively.