Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Mechatronics, Electrical Power, and Vehicular Technology
Kautsar, Makna A’raaf
Unknown Affiliation
Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Smart watering of ornamental plants: exploring the potential of decision trees in precision agriculture based on IoT Pratama, Hafiyyan Putra; Hadi Putri, Dewi Indriati; Putri, Hafiziani Eka; Irawan, Elysa Nensy; Kautsar, Makna A’raaf
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 15, No 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2024.963

Abstract

Ornamental plant farmers face various challenges due to climate change and environmental stress that significantly affect plant health and growth. This research overcomes these challenges by developing an intelligent watering system that uses internet of things (IoT) technology and decision trees (DTs) algorithms to optimize the use of planting land by ensuring plants grow in the most optimal conditions, both in terms of water and nutrients and increase land productivity. The system is built by integrating various sensors to monitor soil moisture, air humidity, temperature, and light intensity in real-time. The collected data is used to automate watering schedules and provide recommendations on suitable plant species based on the soil nutrient content of nitrogen (N), phosphorus (P), and potassium (K). The use of the DTs algorithm helps in analyzing the data from the sensors and providing recommendations on the most suitable plants for the land. The smart watering system was tested in three zones, each simulating a different watering scenario, and successfully maintained optimal conditions for plant growth in each zone. The machine learning (ML) model with the DTs algorithm can predict the right type of ornamental plants based on the existing land conditions in three watering zones, with an accuracy of 89 %, 90 %, and 91 %, respectively. Furthermore, farmers can follow these recommendations to minimize damage and death of plants so that the level of productivity on the land becomes optimal.
Co-Authors Elysa Nensy Irawan Hadi Putri, Dewi Indriati Hafiziani Eka Putri, Hafiziani Eka Pratama, Hafiyyan Putra