Online ISSN: 2515-8260

Keywords : Soil Moisture

Performance of Automatic Smart Irrigation System Using GSM

G Ahmed Zeeshan; Dr. R Sundaraguru; Dr.P. Vijayakarthick; Akula Mahesh Kumar

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 1, Pages 2343-2351

Agribusiness recognizes an indispensable activity in making countries. In India, a colossal piece of masses relies upon nation's unforeseen turn of events. In like way the paper goes for affecting improvement business to mind blowing utilizing computerization and Global new turn of events. The proposed article illustrates the design and implementation of smart irrigation system with auto control and monitoring. In this proposed system we integrate the temperature, humidity, soil moisture sensors to monitor the agriculture parameters and monitor on LCD and send alerts. Depends on soil moisture sensor status irrigation pump ON/OFF automatically and send the alerts to authorized person and control the irrigation pump remotely for designing of smarter irrigation system.


G Ahmed Zeeshan; Dr. R Sundaraguru; Dr.P. Vijayakarthick; Vanitha Vani

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 1, Pages 2352-2359

The IOT based Weather Monitoring and Reporting System venture is utilized to get Live detailing of climate conditions. It will monitor temperature, moistness, dampness smoke, and light and downpour level. Assume Scientists/nature investigators need to screen changes in a specific situation like a downpour backwoods. Also, these individuals are from better places anywhere. In the proposed article we monitoring the weather monitor and alerting using different sensors for easy access of the data. Here we proposed LM35,Smoke, humidity, light, soil moisture sensors are measure the temperature, humidity, soil moisture sensor, light status and smoke for smart weather and all the data will post into server using internet of things.



European Journal of Molecular & Clinical Medicine, 2017, Volume 4, Issue 1, Pages 183-189

This Paper introduces the implementation of different supervised learning techniques for producing accurate estimates of ground water, including meteorological and remotely sensed data. The models thus developed can be extended to be used by the personal remote sensing systems developed in the Center for Self-Organizing Intelligent Systems (CSOIS). To analyze these data and to extract relevant features, such as essential climate variables (ECV), specific methodologies need to be exploited.. The new algorithm enhances the temporal resolution of high spatial resolution of soil moisture observations with good quality and can benefit multiple soil moisture-based applications and research