Integration of Cloud Computing, IoT, and Big Data for the Development of a Novel Smart Agriculture Model

Since the traditional data paradigm cannot handle the volume of information generated by IoT (Internet - of -things) gadgets, cloud storage is now required. These data have been examined using big mining techniques. When evaluating the viability of smart agriculture, the Internet of Farmers must use technologies of information and communication (ICT) in their daily lives to get agricultural information. Crop growth observing, fertilizer classification and irrigation support systems use IoT. This article investigates and optimizes the large amounts of data produced during the farming process, but it seems to be analyzing data mining using the automated k-means system according to the maximum speed. The crop growth curve is intended to simulate the earliest K-means methodology. The experimental findings support the idea that clustering algorithms provide overall benefits in the F mutual information of 7.67% and just a decrease in the total period of 0.23 seconds is supported. Because it can more efficiently realize the operational processes of reliable information communication and data processing, the algorithm presented in this article has a major effect on advancing agronomic information technology.