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@article{177816,
        author = {Bobbala Harshitha and Kasoju Sricharan and Mangalaram Vikas goud and Varala Nikhil Reddy and Mohammed Afzal and Dr. M . Ramesh},
        title = {Land Insight Platform Management System using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {3072-3076},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177816},
        abstract = {The AI-driven platform supports real-time monitoring and decision-making processes, thereby optimizing crop yields and minimizing resource wastage. It leverages machine learning algorithms to predict the most suitable fruit crops for specific soil types and weather conditions, ensuring sustainable agriculture practices. The platform also facilitates farmer education and training through its user-friendly interface, providing insights and recommendations for optimal land use. Furthermore, it includes data analytics tools to track and assess long-term soil health and productivity. The case study highlights significant economic benefits for farmers, including increased profitability and reduced input costs. The platform’s design combines key technologies, daily business requirements, and system design principles analysis to ensure efficient land resource allocation.  By fostering a data-driven approach to land management, the platform paves the way for a more resilient and sustainable agricultural sector.},
        keywords = {Land Resource Management, Artificial Intelligence, Information Platform, Machine Learning, Naive Bayes, Support Vector Machine (SVM), System Design, Data Management.},
        month = {May},
        }