Abstract
This proposed of the project introduces an innovative IoT-based AI-driven solution engineered to revolutionize water management in agricultural contexts, catering to both large-scale piped irrigation networks and micro-irrigation systems. Our proposed system leverages cutting-edge AI algorithms to forecast dynamic crop water requirements, seamlessly integrating real-time soil moisture data to optimize water utilization and amplify crop yields. By amalgamating AI-driven predictive analytics with instantaneous sensor feedback, our system facilitates proactive water resource management, curbing wastage and mitigating environmental repercussions. Moreover, the integration of Deep Learning-based pest detection augments the system's capabilities by safeguarding crops against potential threats, thus fostering sustainable agricultural practices. Additionally, our solution pioneers the incorporation of Deep Learning-based crop production for the detection of wild animals, thereby enabling timely alerts through buzzer sound notifications, thereby ensuring comprehensive crop protection. This amalgamation of IoT, AI, and Deep Learning technologies promises to significantly enhance agricultural sustainability, optimize resource utilization, and mitigate ecological impact, thus paving the way for a more resilient and productive agricultural ecosystem.
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