International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 01 | Jan 2026
p-ISSN: 2395-0072
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A Review on Dharaansh: The Sustainable Farming Advisor using Machine Learning M. D. CHOUDHARI1, KHUSHI DHABALE2, EKTA SATGHARE3, DIYA TEMBHURNE4, SAHMITA , NINAVE5. 1Assistant Professor AI & DS Department K. D. K. COLLEGE OF ENGINEERING, Nagpur 2,3,4,5 K. D. K. COLLEGE OF ENGINEERING, Nagpur
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ABSTRACT- Dharaansh: A Sustainable Farming Advisor based on machine learning can significantly enhance crop productivity and resource use efficiency while providing site-specific data-driven recommendations to the farmer. The system will integrate real-time and historical data on soil pH, soil temperature, and moisture and key climatic variables like ambient temperature, humidity, and rainfall to produce advisory outputs on suitable crops, alternate cropping options, and fertilizer management strategies. This model is trained using supervised learning algorithms on curated agronomic datasets that comprise soil properties, nutrient levels, and weather patterns to predict the most suitable crops and optimal fertilizer regimes for given field conditions, minimizing the risk of crop failure and overuse of inputs. The system also uses rule-based logic and sensor thresholds that raise flags for highly acidic or alkaline soils, suggesting corrective soil amendments like lime or organic matter to restore soil health. The application combines intelligent recommender techniques to make personalized, context-aware recommendations to support precision agriculture and sustainable farming practices. Overall, this Sustainable Farming Advisor seeks to enable efficient fertilizer application, resilient crop planning in response to changing climate conditions, and accessible real-time decision support for farmers. Keywords: Machine learning, Sustainable farming advisor, soil Ph, Crop recommendation, Smart farming, Sustainable farming, Fertilizer recommendation.
INTRODUCTION Farming is the backbone of India's economy, but millions of farmers continue to grapple with unforeseeable weather, lessening soil fertility, pest attacks, and uncertainty due to a lack of dependable information. To empower farmers on such critical issues, an AI-powered agricultural support system by the name Dharaansh has been developed, known as Dharaansh: The Sustainable Farming Advisor. Dharaansh focuses on accurate crop prediction by analyzing soil properties such as pH and NPK levels along with atmospheric conditions of temperature, humidity, and rainfall to recommend the most suitable crops and reduce the risk of crop failure. It also focuses on soil health and fertility management by providing pH validation, fertility checks, and corrective suggestions that include organic amendments, biofertilizers, and crop rotation. It gives real-time weather advisory services for guidance on scheduling irrigation, harvest, and multi-cropping activities while safeguarding against adverse weather conditions, enabling sustainable and profitable farming because of predictive pest and disease alerts integrated into the system, besides promotion of eco-friendly farming practices. With its regional language support, awareness of government schemes and a user-friendly interface presenting visual reports and insights, Dharaansh helps bridge the gap between traditional farming and modern technology, empowering farmers to achieve productivity, profitability, and long-term sustainability.
LITERATURE REVIEW Most recent work on sustainable crop and fertilizer advisory systems focuses on software-centric, machine-learning models operating on curated datasets rather than live IoT streams. These usually ingest soil test reports of pH, N–P–K, organic carbon, historical weather data, and crop performance records stored in relational databases or CSV files; then apply classification or ensemble algorithms that generate recommendations. Several studies propose web-based or desktop applications where farmers or extension workers would enter soil and climate attributes manually, and the backend ML engine returns
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