scholarly journals Review—Machine Learning Techniques in Wireless Sensor Network Based Precision Agriculture

2020 ◽  
Vol 167 (3) ◽  
pp. 037522 ◽  
Author(s):  
Yemeserach Mekonnen ◽  
Srikanth Namuduri ◽  
Lamar Burton ◽  
Arif Sarwat ◽  
Shekhar Bhansali
Keyword(s):  
Machine Learning ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Precision Agriculture ◽  
Machine Learning Techniques ◽  
Wireless Sensor ◽  
Learning Techniques

Integration of Wireless Sensor Network and IoT for Smart Environment Monitoring System

2021 ◽  
Author(s):  
Qi Yu ◽  
Feng Xiong ◽  
Yiran Wang
Keyword(s):  
Machine Learning ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Agricultural Practices ◽  
Integrated System ◽  
Mobile Technologies ◽  
Machine Learning Techniques ◽  
Wireless Sensor ◽  
Smart Environment ◽  
Environment Monitoring

Air contamination, water waste, and radioactive contamination are significant environmental issues. Adequate supervision is needed to ensure economic sustainability through the preservation of a good society. Environmental tracking has become a Smart environment monitoring (SEM) system in recent times, with developments in the Internet of Things and the creation of advanced detectors. This situation evaluates substantial achievements and scientific studies on SEM, including air quality control, water management, radiation emissions, and agricultural practices. A Wireless sensor network and IoT integrated system for Smart environment monitoring (WSN-IoT-SEC) framework are proposed in this research. The analysis will be divided employing SEM techniques applications, and each aim will then be further studied in terms of the detectors, machine learning models, and classifiers operated. A systematic study was carried out based on the study’s evaluated results and patterns, indicating important suggestions and the SEM analysis’s influence. The researchers have discussed objectively how the advancements in mobile technologies, IoT, and wireless sensor networks allow the control of the atmosphere an intelligent monitoring device. Eventually, the concept of rigorous machine learning techniques has been proposed, denouncing techniques and developing appropriate WSN specifications.

scholarly journals A Cotton High-Efficiency Water-Fertilizer Control System Using Wireless Sensor Network for Precision Agriculture

Processes ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1693
Author(s):  
Chanchan Du ◽  
Lixin Zhang ◽  
Xiao Ma ◽  
Xiaokang Lou ◽  
Yongchao Shan ◽  
...  
Keyword(s):  
Control System ◽  
Decision Support ◽  
Wireless Sensor Network ◽  
Decision Support System ◽  
Sensor Network ◽  
Support System ◽  
Precision Agriculture ◽  
High Efficiency ◽  
Wireless Sensor ◽  
Sensors And Actuators

Scientific researchers have applied newly developed technologies, such as sensors and actuators, to different fields, including environmental monitoring, traffic management, and precision agriculture. Using agricultural technology to assist crop fertilization is an important research innovation that can not only reduce the workload of farmers, but also reduce resource waste and soil pollution. This paper describes the design and development of a water-fertilizer control system based on the soil conductivity threshold. The system uses a low-cost wireless sensor network as a data collection and transmission tool and transmits the data to the decision support system. The decision support system considers the change in soil electrical conductivity (EC) and moisture content to guide the application of water-fertilizer, and then improves the fertilization accuracy of the water-fertilizer control system. In the experiment, the proposed water-fertilizer control system was tested, and it was concluded that, compared with the existing traditional water-fertilizer integration control system, the amount of fertilizer used by the system was reduced by 10.89% on average, and it could save 0.76–0.87 tons of fertilizer throughout the whole growth period of cotton.

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