Implementation of smart indoor agriculture system and predictive analysis
Communications in Computer and Information Science, ISSN: 1865-0937, Vol: 1045, Page: 424-435
2019
- 7Citations
- 12Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
Day by day Indoor Agricultural system is becoming more popular and enhancing agricultural productivity. Smart agriculture systems call on different type of Internet of Things (IoT) capabilities to improve farming production and deliver new monitoring facilities. In Smart agriculture system, sensors are placed within the ground may record real-time data on soil moisture, temperature and pH. The main challenges of a smart agriculture system are the integration of these sensors and tying the sensor data to the analytics driving automation and response activities. When integrated, the use of data analytics can reduce the overall cost of agriculture and contribute to higher production from the same amount of area through precise control of water, fertilizer and light. The aim of this paper is to develop an automatic decision making system to watering, lighting and airing the plants based on sensor data. Finally, the paper gives an idea of a prediction formula to find the value of the sensors which will reduce the cost of the sensor.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85073905325&origin=inward; http://dx.doi.org/10.1007/978-981-13-9939-8_38; http://link.springer.com/10.1007/978-981-13-9939-8_38; http://link.springer.com/content/pdf/10.1007/978-981-13-9939-8_38; https://dx.doi.org/10.1007/978-981-13-9939-8_38; https://link.springer.com/chapter/10.1007/978-981-13-9939-8_38
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know