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@article{197260,
        author = {Prajwal Sanjay Wankhede and Kalpak Madekar and Nishant Tarverkar and Vinay Patankar and Vedant Sarode and Vinay Meshram and Sneha Solankar},
        title = {Soil Quality Detection and Crop Prediction Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {9304-9312},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197260},
        abstract = {This study explored various machine learning (ML) techniques for crop classification, yield prediction, and soil fertility assessment to enhance precision agriculture. Methods such as Support Vector Machines (SVM), Decision Trees, Random Forest, Neural Networks, and ensemble learning approaches have been employed across different agricultural datasets. Key applications include optimizing crop selection based on soil macronutrients, predicting yield variability under climate change scenarios, and automating seed germination assessment. Studies have demonstrated that ML models outperform traditional statistical and process-based models, thereby improving the accuracy of crop suitability, yield forecasting, and price prediction. Hybrid models integrating IoT, deep learning, and geospatial data show promise for real-time agricultural decision-making. Challenges, such as data availability, computational complexity, and farmer adoption, remain, highlighting the need for scalable AI-driven solutions. Future directions include integrating real-time soil monitoring, climate adaptation strategies, and mobile-based AI applications for farmers. This paper further provides visual frameworks and process models for practical implementation of ML in precision agriculture.},
        keywords = {Artificial Intelligence, Crop Prediction, IoT, Machine Learning, Precision Agriculture, Soil Quality Detection.},
        month = {April},
        }