HACE Theorem Based Data Extraction to Enhance Smart Agriculture

In this research article, we present an original big data processing model that makes use of the HACE theorem in order to fully realize the big data revolution's potential and enhance agricultural growth. Additionally, our study suggests a three-tier data mining paradigm for effective on-platform massive dataset storage, processing, and analysis. The information provided by this framework is exact and pertinent to raising agricultural output. The rapid advancement of networking, data storage, and data acquisition capabilities has sped up the spread of big data, which is characterized by its multi-sourced, autonomous, complex, and rapidly expanding datasets, across many research and engineering domains, including the physical, biological, and biomedical sciencesIn this study, we provide the HACE theorem, which clarifies the characteristics of the Big Data revolution, as well as a Big Data processing model from a data mining perspective. Our strategy includes user interest modeling, demand-driven data source gathering, mining, and analysis, as well as security and privacy considerations. We look into the difficulties brought on by the Big Data revolution and the data-driven approach. We apply the k-means and Naive Bayes algorithms to large data clustering and classification, and we use Hadoop's Map Reduce method for big data mining. Big Data, the HACE theorem, Map Reduce in Hadoop, and data mining are key concepts in this context. The adapted proposed model underwent training, validation, and testing where it achieved 98.3% Accuracy.