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@article{203776,
        author = {Shrishant C. Suryawanshi and Shreeyash A. Gaikwad and Atharv M.Chorge and Shubham S. Kesarkar and Yashraj R. kawar and Mrs. Soniya S. Atpadkar},
        title = {AI-Based Crop Recommendation System Using the Random Forest Algorithm},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {294-301},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203776},
        abstract = {In modern agriculture, selecting the right crop based on soil compatibility and economic viability is a critical decision-making process for farmers. This paper introduces an accessible, web-based intelligent crop recommendation system designed to guide users toward the most suitable and profitable crops for cultivation. Instead of relying on expensive automated hardware, the system utilises a user-friendly website interface where farmers or agricultural analysts manually input key soil parameters, specifically pH level, Nitrogen (N), Phosphorus (P), and Potassium (K) values. The backend framework integrates these chemical soil metrics with current crop market price data, utilising a Random Forest machine learning algorithm to process the multi-dimensional dataset. By leveraging the ensemble learning capabilities of the Random Forest classifier, the model effectively maps complex environmental variables to predict the most biologically compatible and financially lucrative crop. Experimental results demonstrate that the Random Forest model achieves a high predictive accuracy of [Insert your accuracy, e.g., 96.5%], outperforming traditional classification models. The developed web application serves as a practical, low-cost, and scalable decision-support tool that minimises the risk of crop failure while boosting farmers' income.},
        keywords = {Web-based System, Crop Recommendation, Random Forest Algorithm, Soil Analytics (NPK), pH Level, Market Price Analytics, Precision Agriculture.},
        month = {June},
        }