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@article{182766,
        author = {Swapnil Haribhau Kudke and Krushna Shalikram Telangre and Prof. Yogesh Gajanan Katole and Prof. Abhijit Dnyaneshwar Wanare and Mohd. Razik Mohd. Iqbal},
        title = {Real-time Crop Monitoring Using Edge AI and Wireless Sensor Networks: A Comprehensive Framework for Precision Agriculture},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {3217-3224},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182766},
        abstract = {This research introduces an innovative architecture for real-time crop monitoring that combines edge artificial intelligence (AI) with wireless sensor networks (WSNs) to facilitate precision agriculture. The suggested system tackles the significant issues of latency, bandwidth constraints, and energy efficiency in agricultural monitoring by local data processing at edge nodes. Our design integrates distributed sensor networks with lightweight machine learning models implemented at the edge, facilitating real-time decision-making for crop health evaluation, irrigation control, and yield forecasting. Experimental findings indicate a 78% decrease in data transmission overhead, a 45% increase in response time, and a 67% improvement in energy efficiency relative to conventional cloud-based methods. The system attains 92.3% accuracy in crop health categorization and 89.7% precision in anomaly identification, rendering it appropriate for extensive agricultural implementation.},
        keywords = {Edge AI, Wireless Sensor Networks, Precision Agriculture, Crop Monitoring, IoT, Machine Learning},
        month = {July},
        }