Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{203315,
        author = {Aniket Vajire},
        title = {Smart Crop Advisory System for Sustainable Seed Cultivation Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {10581-10588},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203315},
        abstract = {Efficient crop and fertilizer selection is a critical challenge in precision agriculture, as it depends on multiple interrelated soil and environmental factors. This paper presents a Smart Crop Advisory System designed to support agricultural decision-making by predicting suitable crops and fertilizers based on soil nutrient composition and prevailing meteorological conditions. The proposed approach utilizes a structured dataset comprising temperature, humidity, soil moisture, soil type, crop type, and essential macronutrients including nitrogen, phosphorus, and potassium.To evaluate predictive performance, several machine learning techniques were implemented, namely CatBoost Classifier, Artificial Neural Network (ANN), Support Vector Machine (SVM) with radial basis function and polynomial kernels, and a Hybrid Ensemble Model. Model effectiveness was measured using standard evaluation metrics such as accuracy, precision, recall, and F1-score. Experimental analysis indicates that the Hybrid Ensemble Model achieves superior classification accuracy and more balanced performance compared to individual learning models. CatBoost demonstrates strong effectiveness in handling categorical attributes, while ANN successfully captures nonlinear relationships among input features.The results highlight the importance of combining soil nutrient parameters with weather data to improve recommendation accuracy. The proposed system offers a reliable and data-driven solution for crop and fertilizer selection, contributing to improved productivity, efficient resource utilization, and sustainable agricultural practices.},
        keywords = {CatBoost Classifier,SVM, Artificial Neural Network, Machine Learning, Ensemble Learning},
        month = {May},
        }