International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Agriculture faces pressing challenges such as climate change, water scarcity, and crop diseases, demanding efficient and sustainable solutions. This research presents a smart farming system that integrates IoT sensors, edge computing, and artificial intelligence for real-time monitoring and decision-making. Environmental factors like soil moisture, temperature, humidity, and pH are continuously tracked, while on-site crop leaf images enable disease detection. A deep learning model deployed on edge devices ensures offline functionality, reducing reliance on internet connectivity. The system transitions from MobileNetV2 to the more accurate EfficientNetB3 architecture, achieving improved performance without sacrificing efficiency. This integration enhances productivity, optimizes water usage, and supports timely interventions. Designed for scalability and cost-effectiveness, it offers practical benefits to small and mid-scale farmers. By merging AI with IoT-based sensing, the approach transforms traditional agriculture into a smarter, more resilient practice.

Keywords: Internet of Things (IoT), Edge Artificial Intelligence (Edge AI), MobileNetV2, Convolutional Neural Networks (CNN), Raspberry Pi

Downloads: | DOI: 10.17148/IJARCCE.2025.14913