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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63609
Volume:
10 |
5 |
May 2026
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
COMPUTER ENGINEERING
Matoshri College of Engineering and Research Centre Eklahare
COMPUTER ENGINEERING
Matoshri College of Engineering and Research Centre Eklahare
COMPUTER ENGINEERING
Matoshri College of Engineering and Research Centre Eklahare
COMPUTER ENGINEERING
Matoshri College of Engineering and Research Centre Eklahare
COMPUTER ENGINEERING
Matoshri College of Engineering and Research Centre Eklahare
While AI-powered plant disease identification has advanced rapidly, existing systems typically stop at diagnosis—leaving farmers without actionable guidance, weather-aware planning, market pricing intelligence, or the ability to ask follow-up questions. This paper presents KrushiSetu, a comprehensive Agricultural Decision Support Platform that extends beyond disease classification to provide an integrated ecosystem of farmer-centric services on a single mobile application. The platform integrates five key capabilities: (1) a context-aware Conversational AI Advisor with persistent chat memory, enabling farmers to ask follow-up questions about diagnosed diseases across sessions; (2) a Weather-Based Disease Risk Forecasting engine that correlates 5-day weather data from the OpenWeatherMap API with phytopathological risk models to predict fungal disease risk levels (Low/Medium/High); (3) Real-Time Mandi (Agricultural Market) Price Integration using India's national data.gov.in API, with AI-powered multilingual search translation enabling vernacular queries; (4) a Persistent Scan History System that stores diagnosis results, leaf images, and conversation logs locally for longitudinal crop health tracking; and (5) a fully Trilingual User Interface (English, Hindi, Marathi) built with Jetpack Compose and Material Design 3, featuring animated onboarding, dark/light theme support, and voice input for field use. The platform is implemented as a native Android application using the MVVM architecture pattern with Kotlin Coroutines, DataStore Preferences, and Ktor HTTP networking. By unifying disease diagnosis with weather intelligence, market data, and conversational AI into a single accessible interface, KrushiSetu transforms the farmer's smartphone into a holistic agricultural companion that supports the complete crop management lifecycle.
KEYWORDS
KrushiSetu demonstrates that effective agricultural technology must extend beyond disease identification to provide holistic decision support. By integrating context-aware conversational AI with persistent memory, weather-based disease risk forecasting, real-time market intelligence with AI-translated vernacular search, and a fully trilingual interface, the platform addresses the complete crop management lifecycle within a single smartphone application. The persistent chat memory enables natural, multi-turn advisory conversations across sessions. The weather-disease risk correlation provides actionable spray timing and proactive monitoring guidance. The Mandi price integration with AI-powered multilingual search removes language barriers from market intelligence access. Together, these capabilities transform a disease scanner into a comprehensive agricultural companion, empowering India's farming community with accessible, expert-grade decision support in their own language.
● Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). "Using Deep Learning for Image-Based Plant Disease Detection." Frontiers in Plant Science, 7:1419. ● Team Gemini, Google DeepMind (2024). "Gemini: A Family of Highly Capable Multimodal Models." arXiv:2312.11805. ● Magarey, R. D., Sutton, T. B., & Thayer, C. L. (2005). "A Simple Generic Infection Model for Foliar Fungal Plant Pathogens." Phytopathology, 95(1), 92–100. ● Trendov, N. M., Varas, S., & Zeng, M. (2019). "Digital Technologies in Agriculture and Rural Areas." FAO Briefing Paper. ● TensorFlow (2025). TensorFlow: Open-Source ML Framework. https://www.tensorflow.org ● OpenWeatherMap (2025). Weather API for Real-Time and Forecast Data. https://openweathermap.org ● Government of India (2025). Open Government Data Platform India. https://data.gov.in ● Groq (2025). Groq: Ultra-Fast AI Inference Platform. https://groq.com
Publication Date
5/21/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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