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@article{178003,
        author = {Rakesh H C and Sahana J and Pranav H Ravi and Vijesh H D and Nivyashree R},
        title = {GreenMind: Smart Solutions for Modern Farming},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {6324-6332},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178003},
        abstract = {Agriculture continues to serve as a vital pillar of human development, supporting global needs through the provision of food, raw materials, and livelihoods. Despite its importance, the agricultural sector—particularly for small and marginal farmers—faces persistent hurdles such as declining soil fertility, unpredictable weather conditions, pest outbreaks, and inefficient resource management. These challenges significantly affect crop productivity and overall sustainability, necessitating innovative approaches that bridge traditional farming with mod- ern technological solutions.
This research presents an integrated, AI-powered system designed to support farmers by combining crop prediction, soil health evaluation, and disease detection into a single, accessible platform. The system leverages machine learning and deep learning techniques to deliver data-driven guidance across the agricultural cycle. A Random Forest-based crop prediction model suggests the most suitable crop based on environmental and soil parameters. Soil health monitoring is facilitated using a fertilizer recommendation system derived from curated datasets and structured logic. Furthermore, a Convolutional Neural Network (CNN) is employed to detect crop diseases from plant leaf images, offering instant diagnosis and treatment suggestions. The entire system is developed using React for the frontend interface, Flask for backend processing, and TensorFlow for model deployment. By integrating these components, the proposed solution ad- dresses multiple facets of farm management, helping farmers make informed decisions to improve yield, maintain soil quality, and prevent crop loss due to disease. This approach aims to enhance agricultural efficiency, minimize environmental impact, and support sustainable farming practices tailored to real-world
conditions.},
        keywords = {agriculture sustainability, predictive analytics, smart irrigation, soil health monitoring, IoT in farming, machine learning in agriculture, resource efficiency, small-scale farming, climate-resilient agriculture, precision farming},
        month = {May},
        }