Answers are grounded in this paper and cite their sources.
Smallholder and mid-scale farmers across emerging economies continue to face fragmented access to markets, delayed agronomic knowledge, opaque pricing, and limited digital infrastructure connecting them to vendors, consumers, and institutions. Existing agricultural technology platforms typically address a single slice of this problem — a marketplace, a chatbot, or a sensor dashboard — but rarely unify commerce, knowledge exchange, and decision-support inside one coherent, socially-driven ecosystem. This paper proposes Khetify, an AI-powered social commerce and intelligent agricultural ecosystem that integrates farmers, vendors, consumers, agricultural experts, logistics partners, financial institutions, and government agencies within a single microservices-based platform combining social networking, e-commerce, computer-vision-based crop diagnostics, large language model (LLM) advisory agents, IoT sensor ingestion, and blockchain-backed traceability. We present the system architecture, database schema, AI subsystem design (disease detection, yield and price forecasting, recommendation engine, retrieval-augmented generation chatbot, explainable AI layer), and a blockchain/IoT integration model. We further propose an experimental evaluation protocol using established public datasets (PlantVillage, PlantDoc, and open crop-recommendation and market-price datasets), define evaluation metrics for each subsystem, and report projected performance targets grounded in the published literature rather than measured outcomes, since Khetify has not yet undergone full-scale field deployment. We conclude with a discussion of technical, ethical, and adoption limitations and outline future work spanning digital twin farms, autonomous field robotics, satellite integration, and carbon credit marketplaces. The contribution of this work is a validated, literature-grounded architectural blueprint and research agenda for unifying social commerce and intelligent agriculture, intended to guide subsequent empirical studies and phased deployment.
No takes yet. Share an insight, caveat, or question.
Abhishek Rupesh Thakar (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: