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Implementation of Smart Irrigation System Using Intelligent Systems and Machine Learning Approaches
| Content Provider | Scilit |
|---|---|
| Author | Singh, Raghuraj Deshwal, Ashutosh Kumar, Kuldeep |
| Copyright Year | 2021 |
| Description | In India, agriculture plays an influential role for development in food mass production and also for the economy and development of a country. In the agriculture's land, utilization of right mechanism of irrigation plays a paramount job. Indian agriculture is mainly subservient on the monsoon, which is not a reliable source of water. Therefore there is a need for a smart irrigation system in the country that can distribute ample amounts of water to fields or farms to their soil's moisture content. Numerous zones of agricultural lands are effectually higher than or beneath irrigation due to spatial fluctuation in water infiltration and runoff of rainfall and irrigation. Beneath-irrigated farmland is subjugate to water stress, emerging in production dropping, while over-irrigated zones endure nutrient leaching and plant ailment. Pertinent soil moisture level is obligatory for ideal plant growth. As such, water is a prerequisite element for life sustenance; hence there is a necessity to circumvent its undue handling. Irrigation is an ascendant consumer of water, which devours lots of groundwater. It requires synchronization of waterworks for irrigation. This task ultimate is suited for those regions where water amount is scant and where it has to be used in a reserved amount. Also, this prototype can be afforded by other countries of the world. With the use of sensors, which do no cost much and with simple circuitry, this trial focuses on a low-priced solution that may be acquired by a poor farmer and easy to implement. In this article, some machine learning approaches are also used in predicting the rainfall in nearby areas and also the data collected by the sensors are also visualized. Book Name: Data Science and Innovations for Intelligent Systems |
| Related Links | https://api.taylorfrancis.com/content/chapters/edit/download?identifierName=doi&identifierValue=10.1201/9781003132080-13&type=chapterpdf |
| Ending Page | 318 |
| Page Count | 20 |
| Starting Page | 299 |
| DOI | 10.1201/9781003132080-13 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2021-08-09 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Data Science and Innovations for Intelligent Systems Rainfall Irrigation Agriculture Stress Machine Learning Approaches |
| Content Type | Text |
| Resource Type | Chapter |
