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Internet of Things based Automated Irrigation System for Growing Grapes
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 1 |
| Year of Publication: 2024 |
| Authors: Moechammad Sarosa, Erlita Putri Wahyu, Agil Evan |
10.5120/ijca2024923344
|
Moechammad Sarosa, Erlita Putri Wahyu, Agil Evan . Internet of Things based Automated Irrigation System for Growing Grapes. International Journal of Computer Applications. 186, 1 ( Jan 2024), 44-48. DOI=10.5120/ijca2024923344
Abstract
Grape growing requires care and diligence in providing nutrients to feed the plants and watering according to the plants' needs to maximized growth. This research develops a system to meet these needs, namely a system for watering according to the needs of the plants, i.e. according to soil moisture. The moisture sensor is used to determine the water requirements of the plants, and when the sensor indicates that the water content in the planting medium is less than the specified value, the system delivers water to the planting medium. To know the nutrients needed by the plants, this system is equipped with an NPK sensor, and based on the readings of the NPK sensor, nutrients that are lacking in the planting medium are added by mixing nutrients with the water for irrigation. In order to monitor the development of the vines, this system has also been equipped with a camera so that the owners of the vines can take pictures of their plants using Android devices connected to the irrigation system using Internet of Thing technology. The results of the tests carried out show that. Based on the results and tests carried out on the irrigation system, several conclusions can be drawn, including the following: the irrigation system has been able to function properly using several sensor components, namely the soil moisture sensor, the NPK sensor and the camera module connected to the ESP8266 microcontroller. The average automatic watering time is ± 6 seconds, the soil moisture is ± 60% and the accuracy of the readings from each sensor is ± 99%. During the 30 days of testing, vine A (manual) experienced a growth of ± 4.3 cm while vine B (automatic) experienced a growth of ± 5.2 cm.
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