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@article{210775,
        author = {B. Jampanna and Dr. Bonthu Kotaiah},
        title = {A Comparative Study of Machine Learning Algorithms for AI-Based Crop Recommendation Using Soil and Climatic Parameters},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {6},
        pages = {2179-2183},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=210775},
        abstract = {Choosing the right crop to grow can be one of the most important decisions a farmer makes. Usually, this decision is done out of habit. This paper treats crop selection as a multi-class classification task and compares four supervised learners: Decision Tree, K-Nearest Neighbours (KNN), Support Vector Machine (SVM) and Random Forest. Each model receives seven inputs: The models are given inputs of nitrogen, phosphorus, potassium, temperature, relative humidity, soil pH and rainfall. The output is the ccrop label. All experiments were run in Python with Scikit-learn on Google Colab, using one shared preprocessing pipeline and an identical stratified train/test split, so that score differences reflect the algorithms and not the setup. Performance is reported as accuracy, precision, recall, F1-score and a confusion matrix. In our runs Random Forest scored highest (accuracy 0.992), ahead of the Decision Tree (0.985), SVM (0.979) and KNN (0.978). The results suggest that machine learning can serve as a practical decision-support aid for farmers and advisers.},
        keywords = {crop recommendation, precision agriculture, machine learning, Random0 Forest, Decision Tree, KNN, SVM, soil parameters, climatic parameters},
        month = {November},
        }