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@article{177700,
        author = {Naveen Raj M and Yogeshwaran G and SUJITHA S and Ragavan. R and ADHITHYAN R},
        title = {Crop Prediction and Fertilizer Recommendation Using Machine Learning: Enhancing Agricultural Decision-Making for Indian Farmers},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {1244-1249},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177700},
        abstract = {Agriculture forms the backbone of the Indian economy, with a significant portion of the population reliant on it for livelihood. However, traditional farming practices often face challenges related to optimizing crop selection and resource management, impacting yield and sustainability. This paper presents a web-based decision support system, "Crop Prediction & Fertilizer Recommendation Using Machine Learning," designed to empower Indian farmers with data-driven insights. Leveraging Machine Learning (ML) and potentially Deep Learning (DL) techniques, the system integrates two core modules: Crop Recommendation and Fertilizer Recommendation. The Crop Recommendation module analyzes soil parameters (e.g., N, P, K levels, pH, moisture) and environmental conditions (e.g., temperature, humidity, rainfall) to suggest the most suitable crops for a given plot of land, thereby promoting optimal land utilization. The Fertilizer Recommendation module assesses the nutrient requirements of the selected crop in conjunction with the existing soil nutrient profile to provide tailored advice on fertilizer type and quantity. This facilitates efficient nutrient management, minimizes fertilizer overuse, reduces input costs, and mitigates environmental runoff. The system is developed with a user-friendly interface using HTML, CSS, and JavaScript for the frontend, and Python for the backend processing and ML model implementation. By translating complex AI technology into actionable recommendations, this project aims to enhance farming efficiency, maximize crop yields, promote sustainable agricultural practices, and contribute positively to resource optimization and environmental protection in the Indian agricultural landscape.},
        keywords = {Crop Prediction, Fertilizer Recommendation, Machine Learning, Deep Learning, Agriculture, Yield Maximization, AI Technology, Soil Analysis, Nutrient Management, Resource Optimization, Sustainable Farming, Environmental Protection, Web Application, Data-Driven Decisions, Farming Efficiency, Technological Advancement, Indian Agriculture.},
        month = {May},
        }