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@article{200889,
        author = {Aniket Vajire},
        title = {Smart Crop Advisory System for Sustainable Seed Cultivation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {11536-11542},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200889},
        abstract = {Sustainable agricultural development requires accurate decision-making in seed selection and fertilizer management based on soil health conditions. In many regions, farmers rely on traditional practices that do not consider field-level variability, leading to reduced productivity and soil degradation. This paper presents a Smart Crop Advisory System for Sustainable Seed Cultivation, designed to recommend suitable seed varieties and optimal fertilizer usage using machine learning techniques. The system analyzes soil parameters such as nitrogen, phosphorus, potassium, organic carbon, electrical conductivity, soil pH, and micronutrients including zinc, iron, manganese, copper, boron, and sulphur, along with environmental factors like rainfall, temperature, and humidity. Multiple supervised learning algorithms, including Random Forest, Support Vector Machine, Extreme Gradient Boosting, Artificial Neural Network, and hybrid ensemble models, are implemented and evaluated using performance metrics such as accuracy, precision, recall, and F1-score. The comparative analysis identifies the most reliable model for prediction. The proposed system provides region-specific recommendations for crops such as rice, jowar, bajra, and wheat, ensuring improved yield, efficient fertilizer utilization, and sustainable soil management.},
        keywords = {Smart Agriculture, Crop Advisory System, Machine Learning, Soil Analysis, Seed Recommendation, Fertilizer Prediction, Support Vector Machine, Artificial Neural Network, Ensemble Learning},
        month = {May},
        }