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@article{210708,
        author = {Udipthi S and Smitha D and Ruchitha C and Shwetha M and Dr krishna Kumar P R},
        title = {AgroSense: A Microservice Architecture for Crop Diagnostic and Field Analytics},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {6},
        pages = {1932-1936},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=210708},
        abstract = {AgroSense is a microservice-driven, full-stack framework bridging advanced agricultural AI with rural smallholder accessibility. It integrates five core engines—dual-mode soil analysis, CNN leaf pathology scanning, weather rule downscaling, and localized AI chat—into a React, Node.js, Flask, and SQLite architecture. Featuring “Snap & Detect” camera interactions, color-coded indicators, and Kannada-English support, it streamlines farm analytics.},
        keywords = {Precision Agriculture, Full-Stack Microservices Deep Learning and Computer Vision, Agrometeorological Decision Support, Human-Cantered Rural Interface},
        month = {November},
        }