An Intelligent Irrigation System and Prediction of Environmental Weather Based on Nano Electronics and Internet of Things Devices

被引:3
作者
Ahmad, Sultan [1 ,3 ]
Uddin, Mohammed Yousuf [2 ]
机构
[1] Prince Sattam Bin Abdulaziz Univ, Coll Comp Engn & Sci, Dept Comp Sci, Alkharj 11942, Saudi Arabia
[2] Prince Sattam Bin Abdulaziz Univ, Coll Comp Engn & Sci, Dept Informat Syst, Alkharj 11942, Saudi Arabia
[3] Chandigarh Univ, Univ Ctr Res & Dev UCRD, Dept Comp Sci & Engn, Mohali 140413, Punjab, India
关键词
Internet of Things; Nanoelectronics Devices; Multi-Strategic Gradient Salp Swarm Optimization; Environmental Monitoring; Weather Monitoring and Prediction; DR-LeNet Classifier; IOT; AGRICULTURE; ARDUINO;
D O I
10.1166/jno.2023.3382
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Internet of Things (IoT) shows a significant aspect in day-to-day life like Health monitoring, Environmen-tal monitoring, vehicle monitoring, crop monitoring, material science and other applications. The proposed method objective is to monitor the environmental factors periodically on analysing the data collected from sensors and Nano electronics devices. IoT dependent smart irrigation system could aids in attaining the optimal resource utilization in the precise farming. The proposed system intelligence depends on smarter algorithm that considers sensed data with weather forecast parameters such as moisture, air temperature, humidity, precipitation in near future. Primarily, the input data is acquired from sensors placed at transmitter side that is connected to microcontroller and attained data is thus stored in the cloud paradigm from which the data is monitored and processed for making further decision. At the side of receiver module, the data collected is thus preprocessed. The features are extracted using Adaptive Fisher discriminant analysis. The optimum best features were chosen by employing optmization process with the help of multi-strategic gra-IP: 2038 10920 On Fri 19 May 2023 14 54:14 dient Salp swarm optimization (MSG-SSOA). Finally, the Deep residual LeNet classifier is employed for the Copyright: American Scientific Publishers classification of sensed data. Consequently, for thffectual performance assessment of suggested strategy De ivered by Ingenta the existing methods are related with proposed methods to validate the proposed system effectiveness.
引用
收藏
页码:227 / 236
页数:10
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