Remote Monitoring and Management System of Intelligent Agriculture under the Internet of Things and Deep Learning

• Published in: Wireless communications and mobile computing Vol. 2022; pp. 1 - 13
• Main Authors: Zhu, Meirong, Shang, Jie
• Format: Journal Article
• Language: English
• Published: Oxford Hindawi 23.05.2022; Hindawi Limited
• Subjects: Agricultural management; Agricultural production; Agriculture; Air temperature; Algorithms; Carbon dioxide; Control systems design; Data centers; Data management; Data storage; Deep learning; Demand analysis; Greenhouses; Internet of Things; Machine learning; Modules; Outliers (statistics); Planting; Remote monitoring; China
• ISSN: 1530-8669; 1530-8677
• DOI: 10.1155/2022/1206677

Based on the Internet of Things (IoT) technology and deep learning algorithm, a greenhouse intelligent agriculture management system was established to analyse the application value of the intelligent agriculture remote monitoring management system in the greenhouse planting industry. Based on the analysis of greenhouse planting demand and environmental factors, the intelligent agriculture monitoring system is established based on the IoT, and the greenhouse system controller is designed based on the adaptive proportion integration differentiation (PID) algorithm. The noise data removal method is established based on the furthest priority strategy k-means (FPKM) algorithm, and the greenhouse data management system is established mainly by the business platform and management platform. The data set of air temperature during the cultivation of Flammulina velutifolia in a factory from October 2020 to January 2021 was selected as the research data to analyse the ability of the IoT-based IARMM system to collect greenhouse temperature, carbon dioxide, and light data. In addition, the application of the greenhouse data management system in greenhouse data monitoring and control is analysed. The processing capability of agricultural environment monitoring data based on the FPKM algorithm is analysed. The results show that the intelligent agriculture monitoring system based on IoT and machine learning can effectively monitor the data on greenhouse temperature, carbon dioxide, light, and other environmental factors, and the greenhouse data management system can effectively ensure the normal operation of equipment and data storage. After being processed by the FPKM algorithm, outliers are identified and effectively removed. Under random seeds, the iteration times of the FPKM algorithm and the k-means algorithm are significantly different. The iteration number of the FPKM algorithm is basically stable at approximately 2 times, while the iteration number of the k-means algorithm obviously fluctuates. Based on the IoT and FPKM algorithm, the intelligent agriculture monitoring system covering the user monitoring center, data center module, and mobile phone client module is established. This work establishes a practical remote monitoring and management system for intelligent agriculture based on the IoT and machine learning algorithm, which provides a new idea for intelligent agricultural management.