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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64822
Volume:
10 |
6 |
Jun 2026
Pages:
1-7
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.
Information Technology
Sinhgad College Of Engineering, Vadgaon
Information Technology
Sinhgad College Of Engineering, Vadgaon
Information Technology
Sinhgad College Of Engineering, Vadgaon
Information Technology
Sinhgad College Of Engineering, Vadgaon
Information Technology
Sinhgad College Of Engineering, Vadgaon
The Smart Farming Assistant is an Artificial Intelligence (AI)-driven platform designed to empower small and marginal farmers by offering intelligent crop advisory, contract farming opportunities, and real-time farm management support. The system utilizes soil testing reports and AI-based analysis, handled by the Crop Prediction Module, to recommend the most suitable crops and generate personalized farm schedules covering all stages from sowing to harvesting. A crucial element is the Contract Farming Module, which enables farmers to view and apply for contracts published by agro-based companies, ensuring assured markets and fair pricing. The platform also integrates the Government Scheme Information Module, real-time market intelligence, and provides an offline mode for users in low-connectivity rural areas. By focusing on digital intelligence, market access, and structured financial mechanisms, the Smart Farming Assistant bridges the gap between traditional farming and modern digital agriculture, enhancing profitability, efficiency, and sustainability for farmers
KEYWORDS
Agriculture continues to play a vital role in economic growth and food security, making it essential to adopt modern technologies that support farmers in making informed decisions. The proposed SmartCrop Advisor: AI-Based Crop Prediction and Contract Farming Platform successfully integrates machine learning techniques with web-based technologies to address two major agricultural challenges: crop selection and market accessibility. The developed system utilizes a Random Forest machine learning model to analyze important agricultural parameters such as soil nutrients, temperature, humidity, rainfall, and pH values, and provides accurate crop recommendations to farmers. This helps reduce uncertainty in crop selection and enables farmers to improve productivity through data-driven decision-making. The contract farming module further enhances the platform by creating a digital bridge between farmers and companies, allowing transparent communication, efficient contract management, and better market opportunities. The implementation of role-based access for Farmers, Companies, and Administrators ensures secure and efficient management of platform activities. The system successfully provides functionalities such as crop prediction, contract creation, company verification, user management, and administrative monitoring through an intuitive and user-friendly interface. The integration of React.js, Node.js, Express.js, MongoDB, and Python-based machine learning technologies results in a scalable and reliable agricultural support platform. The evaluation of the system demonstrates that SmartCrop Advisor effectively assists farmers in selecting suitable crops and accessing contract farming opportunities. The platform improves transparency, enhances collaboration among stakeholders, and contributes to sustainable agricultural development. By combining intelligent crop prediction with digital contract farming services, the proposed solution offers a practical and efficient approach to modernizing agricultural practices. In conclusion, SmartCrop Advisor provides an innovative and technology-driven solution that empowers farmers, strengthens farmer-company relationships, and promotes efficient resource utilization. The successful implementation of the system highlights the potential of Artificial Intelligence and Machine Learning in transforming the agricultural sector and supporting the development of a smarter and more sustainable farming ecosystem.
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Publication Date
6/12/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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