AI-POWERED SMART AGRICULTURE

Agriculture is influenced by changing weather, soil conditions, crop selection, plant disease, and market variability. Farmers
may need to consult multiple sources to obtain timely and understandable guidance. This paper presents a proposed AIpowered smart agriculture framework that combines farmer-provided information with weather services, crop and soil
knowledge, plant-health image analysis, and market information. A web or mobile interface collects a location, crop details,
and the farmer’s concern. An application layer validates the inputs and coordinates the available services. The
recommendation module then presents context-sensitive suggestions with their data sources, confidence indicators where
supported, and appropriate cautions. The design emphasizes explainability, accessibility, privacy, and the involvement of
local agricultural experts for consequential decisions. A prototype may be implemented using a REST API, a structured
knowledge base, and separately deployable analytical services. Evaluation should measure functional correctness, response
time, recommendation relevance, usability, and robustness under missing or outdated data. Because this paper proposes a
framework rather than reporting completed field trials, no experimental accuracy or yield improvement is claimed.