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@article{197653,
        author = {Dr. MK Jayanthi Kannan and JAYANT CHOUDHARY and ADYA GUPTA and JIHI MAMTANI and DAMAN and LOKESH},
        title = {Smart Agri-Assist: An Integrated IoT-CNN Framework for Real-Time Crop Health Monitoring, Disease Detection, and Precision Advisory in Low-Connectivity Rural Environments},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {8091-8099},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197653},
        abstract = {Smart Agri-Assist is a next-generation, AI-driven, and IoT-enabled smart agriculture system designed to transform traditional farming into a data-driven, efficient, and sustainable practice. The platform integrates real-time environmental sensing, machine-learning-based disease detection, and intelligent advisory systems into a unified ecosystem to empower farmers. By leveraging IoT sensors, such as soil moisture and temperature-humidity modules, along with AI models like Convolutional Neural Networks (CNNs), the system continuously monitors crop health and environmental conditions. Designed for rural accessibility, it operates effectively in limited-connectivity environments to provide localized, easy-to-understand recommendations that improve yield and reduce operational costs. Agriculture remains the backbone of developing economies, yet traditional farming practices face systemic challenges including delayed disease detection, inefficient resource utilization, and limited access to real-time environmental data. These inefficiencies lead to preventable crop losses, excessive water and fertilizer usage, and reduced farmer profitability. This research presents Smart Agri-Assist, a next-generation AI-driven and IoT-enabled smart agriculture system that integrates real-time environmental sensing, machine learning-based disease detection, and intelligent advisory capabilities into a unified, accessible ecosystem designed for rural deployment. The system comprises three integrated modules:  an IoT sensor network capturing soil moisture, temperature, humidity, and ambient light; a Convolutional Neural Network (CNN) for plant disease detection from leaf images; and a rule-based and ML-enhanced advisory engine generating localized recommendations. The system incorporates edge computing capabilities and store-and-forward synchronization for operation in limited-connectivity environments.},
        keywords = {AI in Agriculture, IoT, Convolutional Neural Networks (CNN), Precision Farming, Disease Detection, Sustainable Agriculture, Edge Computing, Rural Connectivity.},
        month = {April},
        }