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@article{177308,
        author = {Stuti Shah and Ms. Nirali Kapadia and Mr. Kamal Manek},
        title = {Holistic Crop Recommendation Systems Driven by Predictive Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {1872-1877},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177308},
        abstract = {Agriculture plays a significant role in the Indian economy, yet farmers often struggle with selecting the appropriate crop for their soil. Precision agriculture addresses this issue by utilizing research data to suggest suitable crops based on specific parameters, reducing productivity setbacks and increasing yield. An intelligent system is being developed to aid Indian farmers in making informed decisions regarding crop selection, considering factors such as sowing season, geographical location, and soil attributes, with yield predictions for the recommended crop included. The hybrid model incorporating a classifier machine learning algorithm serves as a pivotal tool in recommending optimal crops by analysing various agronomic parameters, thereby significantly enhancing the agricultural productivity and economic prospects of India. By systematically ranking land conditions, the model not only facilitates the identification of crop quality but also strengthens decision- making processes for farmers, enabling them to make informed choices based on empirical data. Furthermore, the integration of ensembling classifiers elevates prediction accuracy, offering robust insights into crop selection and yield potential. The informed ranking mechanism not only assists in selecting suitable crops but also aids in cost prediction, ultimately contributing to more sustainable agricultural practices and improved livelihoods in rural communities},
        keywords = {Crop selection, Machine learning, Ensembling classifiers.},
        month = {May},
        }