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@article{201815,
        author = {Mr. Mohit Nakahate and Dr. K. S. Chandawani and Prof. S. S. Chahande and Mr. Harshal Shende and Mr. Himanshu Potdar and Mr. Jay Panchabhai and Mr. Mayur Nagpure and Mr. Karan Sable},
        title = {AGRICULTURAL CROP RECOMMENDATION SYSTEM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {6941-6945},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201815},
        abstract = {Agriculture is a fundamental sector that supports the livelihood of a large population, especially in developing countries. However, farmers often face difficulties in selecting appropriate crops due to a lack of scientific knowledge about soil fertility and changing climatic conditions. Traditional farming practices are mainly based on experience and intuition, which may result in improper crop selection, reduced yield, inefficient use of resources, and financial losses. There is a growing need for intelligent and data-driven systems that can assist farmers in making accurate crop selection decisions.  This project presents an Agricultural Crop Recommendation System that utilizes machine learning techniques to recommend the most suitable crops based on soil and environmental parameters. The system analyses key factors such as Nitrogen (N), Phosphorus (P), Potassium (K), soil pH, rainfall, temperature, and humidity. Various supervised machine learning algorithms are studied and implemented, and a Random Forest classifier is selected due to its higher accuracy, robustness, and ability to handle complex agricultural data. The model is trained using a real-world agricultural dataset obtained from Kaggle. 
The proposed system is deployed as a Flask-based web application, providing a user-friendly interface for farmers to input soil and climatic data and receive real-time crop recommendations. This system helps reduce the risk of crop failure, improves agricultural productivity, and supports sustainable and precision farming practices. By enabling data-driven decision-making, the Agricultural Crop Recommendation System serves as an effective decision-support tool that can enhance crop planning, resource utilization, and overall farm profitability.},
        keywords = {Random Forest Algorithm, Flask Framework, Kaggle Dataset, Supervised Learning},
        month = {May},
        }