ClassEdge: AI Assessment and Analytics System of Data-driven Teaching
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Abstract
The study deals with the information asymmetry of the agricultural sector in Kerala where more than 70 percent of the landholdings are marginal. Conventional online advisories are not always successful because of the generic data and language barrier. This paper suggests Krishi Sakhi, which is a personal farming assistant applied using a Malayalam-based Natural Language Processing (NLP) pipeline. The system can offer proactive irrigation, pest management and market timing decision support by synthesizing real-time data of the IMD, ICAR and eNAM. The paper describes the system architecture, multi-source institutional datasets integration, and voice-first interface implementation that makes the system accessible to low literacy users. Findings show that there has been a great enhancement in the maintenance of digital records as well as a shortening of the response time to local threats to crops
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