Agriculture data analysis using parallel k-nearest neighbour classification algorithm

• Published in: International journal of reconfigurable and embedded systems Vol. 13; no. 2; p. 332
• Main Authors: Muninarayanappa, Vimala, Ranjan, Rajeev
• Format: Journal Article
• Language: English
• Published: Yogyakarta IAES Institute of Advanced Engineering and Science 01.07.2024
• Subjects: Agricultural production; Agriculture; Algorithms; Architecture; Artificial intelligence; Big Data; Classification; Cloud computing; Cluster analysis; Clustering; Computer science; Computing time; Coronaviruses; Data analysis; Data processing; Efficiency; Embedded systems; Geographic information systems; Humidity; Internet of Things; Machine learning; Multidimensional data; Rain; Rainfall; Real time; Remote sensing; Sensors; Temperature; Unstructured data
• ISSN: 2089-4864; 2722-2608; 2089-4864
• DOI: 10.11591/ijres.v13.i2.pp332-340

A cost-effective and effective agriculture management system is created by utilizing data analytics (DA), internet of things (IoT), and cloud computing (CC). Geographic information system (GIS) technology and remote sensing predictions give users and stakeholders access to a variety of sensory data, including rainfall patterns and weather-related information (such as pressure, humidity, and temperatures). They have unstructured format for sensory data. The current systems do a poor job of analysing such data since they cannot effectively balance speed and memory usage. An effective categorization model (ECM) on agriculture management system is proposed to address this research difficulty. First, a classification technique called priority-based k-nearest neighbour (KNN) is provided to categorize unstructured multi-dimensional data into a structured form. Additionally, the Hadoop MapReduce (HMR) framework is used to do classification utilizing a parallel approach. Data from real-time IoT sensors used in agriculture is the subject of experiments. The suggested approach significantly outperforms previous approaches that are computing time, memory efficiency, model accuracy, and speedup.