Abstract
The abstract discusses the design of smart agriculture using big data and Internet of Things (IoT) technologies to enhance real-time data communication and information processing in agriculture. It focuses on optimizing data storage, processing, and mining of large agricultural data, utilizing the k-means algorithm for data mining. The study shows improved efficiency in real-time data processing and communication, contributing to agricultural modernization and informatization.
Introduction
The introduction section of the research article discusses the significance of big data in various fields due to the information revolution. It highlights the importance of agricultural informatization, emphasizing the integration of big data analysis and climate change science to enhance climate-smart agriculture and agricultural research. The text also mentions the role of technologies like the Internet of Things (IoT) and cloud computing in advancing intelligent agriculture through the use of information and communication technologies in farm management.
Agriculture Iot system
The Agricultural Internet of Things (IOT) system utilizes sensor nodes and various sensors to collect data in agricultural fields, enabling real-time monitoring and decision-making for farmers. This system integrates technologies like big data analysis, cloud computing, and wireless sensor networks to enhance sustainable agriculture practices and improve crop management efficiency. By leveraging IOT technology, farmers can optimize their production processes, adjust planting plans, and maximize agricultural productivity through data-driven insights.
Big data system
Big data refers to large-scale datasets with specific characteristics like volume, complexity, and value for analysis within a limited timeframe. In the context of big data systems, data mining plays a crucial role in extracting valuable insights and knowledge from vast and diverse datasets. Data mining involves processes like goal setting, data collection, preprocessing, model construction, evaluation, and knowledge representation to uncover hidden patterns and information from big data.
Data mining
Data mining algorithms are tools used to extract patterns and insights from large datasets. They include association rules analysis, clustering analysis, prediction and regression algorithms, and citation sorting algorithms. Clustering analysis, exemplified by the K-means algorithm, groups similar data points together based on their attributes, helping to identify patterns and relationships within the data.
Major design principles
The design principles of a smart agriculture system based on the Internet of Things (IoT) involve integrating IoT technology with agricultural practices to enhance data acquisition, preprocessing, processing, and analysis in farming. IoT technology represents the evolution of modern internet technology and plays a crucial role in the latest generation of information technology, particularly in the context of smart agriculture systems. By utilizing sensors, data processing, and analysis, IoT-based smart agriculture systems can efficiently monitor crop growth, intervene when necessary, and optimize agricultural practices for improved efficiency and productivity.
Experiment implementation
In the context of smart agricultural systems, the experiment simulation involves processing and analyzing a large amount of sensor-collected data to determine the optimal growth environment for seedlings. This simulation aims to cluster temperature and soil sensor data using the k-means algorithm based on maximum distance, which improves clustering efficiency by selecting cluster centers more purposefully. By comparing the K-means algorithm with the maximum distance k-means algorithm using MATLAB, the study demonstrates the effectiveness of the improved algorithm in generating relative optimal environmental values for plant growth.
Results and discussion
The experimental results and discussion in the article focus on using the K-means algorithm and the maximum distance K-means algorithm to analyze sensor data in smart agricultural systems. The K-means algorithm is compared with the improved maximum distance K-means algorithm to determine the optimal growth environment for seedlings based on soil temperature and other environmental factors. The results show that the improved algorithm selects cluster centers more purposefully, leading to fewer iterations and improved clustering efficiency compared to the traditional K-means algorithm.
Conclusion
The conclusion and prospection section of the text discusses the application of IoT and data mining technologies in smart agriculture, specifically focusing on managing agricultural greenhouses through precise control and analysis. It introduces the use of the K-means clustering algorithm for data analysis and optimization in agricultural big data obtained through IoT technology, highlighting the potential for further research using neural networks and classification technologies to predict agricultural product outputs based on sensor data.