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@article{183835,
        author = {Manish Kumar and Prof. Anurag Srivastava},
        title = {A Survey on Soil Fertility and Crop Recommendation Using Machine Learning Technique},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {3181-3185},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183835},
        abstract = {Soil fertility plays a critical role in agricultural productivity and food security. With increasing global population and diminishing arable land, optimizing crop yield through intelligent soil management has become imperative. This survey explores the integration of machine learning (ML) techniques in assessing soil fertility and recommending suitable crops, aiming to enhance sustainable farming practices. The study reviews a broad spectrum of recent works employing supervised, unsupervised, and ensemble learning methods, including decision trees, random forests, support vector machines, and deep learning models. These models utilize soil parameters such as pH, nitrogen, phosphorus, potassium levels, and moisture content to classify soil types and suggest optimal crops for cultivation. The survey highlights the strengths and limitations of various ML algorithms in terms of accuracy, interpretability, and scalability. Additionally, it discusses the challenges of data availability, feature selection, and real-time implementation in field conditions. By analyzing existing approaches, this survey offers insights into future directions and the potential of ML in revolutionizing precision agriculture through automated crop recommendation systems.},
        keywords = {Deep Learning, Fertilizer Recommendation, Crop Recommendation, soil data; soil analysis},
        month = {August},
        }