Intelligent Computing for Crop Monitoring in CIoT: Leveraging AI and Big Data Technologies

Abstract

Consumer Internet of Things (CIoT) has revolutionised agriculture by integrating intelligent computing, artificial intelligence and big data technologies in crop monitoring. This paper explores the application of intelligent computing and deep learning methodologies in crop monitoring within the CIoT framework. In CIoT-based crop monitoring, a vision sensor collects real-time data from crop leaf images. The image dataset is processed using state-of-the-art deep learning models and intelligent computing algorithms. This integration enables the early detection of crop diseases by leveraging computer vision and deep learning. Intelligent computing systems provide accurate disease classification, real-time alerts, and actionable recommendations for optimised crop management practises. This advanced system empowers farmers to make data-driven decisions, such as irrigation optimization, targeted pesticide application and nutrient supplementation, to maximise crop productivity and minimise losses. A benchmark dataset of leaf images is used, and a deep learning based model is presented for classifying healthy and diseased leaves. Experimental results demonstrate an accuracy rate of 0.98, with detailed validation, including dataset size and model parameters. Key benefits of intelligent computing in CIoT-based crop monitoring include enhanced resource efficiency, reduced environmental impact, and improved sustainability. The paper also addresses the challenges of implementing AI and big data technologies, such as data privacy, security, interoperability and resource management in agricultural settings.