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@article{194667,
        author = {Sandip Madhukar Jadhav and Gayatri Pandurang Kotwal and Raj Kiran Kothari and Prof. Ramesh Pandharinath Daund},
        title = {AI-Powered Crop Management and Recommendation System for Intelligent and Sustainable Agriculture},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {4849-4859},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194667},
        abstract = {Agricultural productivity is increasingly affected by climate variability, soil degradation, pest infestations, and volatile market conditions. Farmers, especially in developing regions, often lack access to scientific decision-support systems and real-time agricultural intelligence. This research proposes a comprehensive AI-Powered Crop Management and Recommendation System that integrates machine learning, deep learning, predictive analytics, and real-time API services into a unified platform.
The system provides intelligent crop prediction based on geographical and seasonal attributes, soil-based crop recommendation using NPK and environmental parameters, fertilizer optimization, rainfall forecasting through time-series modeling, crop yield estimation using regression techniques, plant disease detection using convolutional neural networks, real-time agricultural news aggregation, market price monitoring, AI-powered chatbot assistance, and secure digital payment integration.
Unlike isolated agricultural tools, the proposed system integrates predictive modeling, image-based disease diagnosis, and digital commerce into a single ecosystem. Experimental analysis demonstrates improved prediction performance and decision-making efficiency. The proposed solution contributes toward precision agriculture, sustainability, and farmer economic empowerment.
In addition to predictive modeling, the proposed framework also focuses on the integration of intelligent automation and real-time information services that assist farmers in making scientifically informed decisions. The system combines multiple artificial intelligence techniques including supervised learning algorithms, deep learning architectures, and time-series forecasting models to offer comprehensive agricultural intelligence.
By integrating crop prediction, soil-based crop recommendation, fertilizer optimization, rainfall forecasting, yield estimation, disease detection, and market intelligence into a single platform, the system aims to reduce uncertainty in agricultural decision-making. The proposed approach also promotes sustainable farming practices by minimizing the excessive use of fertilizers and enabling early disease identification. Overall, the system demonstrates how artificial intelligence can transform traditional agriculture into a smart and data-driven agricultural ecosystem.},
        keywords = {Artificial Intelligence, Precision Agriculture, Machine Learning, Deep Learning, Crop Recommendation, Yield Prediction, Sustainable Farming, Decision Support Systems, Rainfall Forecasting, Plant Disease Detection, Smart Farming Technologies, Agricultural Data Analytics.},
        month = {March},
        }