Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering International Journal of Reconfigurable and Embedded Systems (IJRES)
MUHAMMAD RISAL
Universitas Handayani Makassar
Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Multi-modal sensor integration in chicken-fish-vegetable greenhouse agriculture based on internet of things Muhammad Risal; Pujianti Wahyuningsih; Suwatri Jura; Irmawaty Iskandar; Abdul Jalil
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v15.i1.pp138-149

Abstract

Integrated chicken-fish-vegetable farming is a type of agriculture that combines the benefits of them within a single ecosystem. The objective of this study is to develop a control and monitoring system for integrated greenhouse-based chicken-fish-vegetable farming using the internet of things (IoT). The monitoring method employs the integration of multi-modal sensors in the greenhouse, consisting of a camera, water level, DHT11, pH, TDS, DS18B20, light dependent resistor (LDR), and infrared (IR) sensor. The camera functions as a visual monitoring tool for the farm, water level sensor detects hydroponic water levels, DHT11 measures air temperature and humidity, pH sensor measures water acidity, TDS sensor detects water nutrients, DS18B20 measures pond water temperature, LDR detects weather conditions, and IR sensor measures sunlight intensity. The processing units used to control the sensors and output devices are the ESP32 and Raspberry Pi. The system outputs include a relay for pump control, an LCD for text messages, and IoT information visualization using the Blynk platform. The results of this study demonstrate that the multi-modal sensor device can effectively monitor the conditions of integrated greenhouse-based chicken-fish-vegetable farming, achieving an accuracy of up to 96%, with an average data transmission time of 6 seconds through the Blynk IoT platform.
Aquaponic greenhouse agriculture integrated with multi-modal sensors and LED-grow-light IoT-based Pujianti Wahyuningsih; Muhammad Risal; Nining Haerani; Abdul Jalil
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp2254-2264

Abstract

This study aims to develop a smart greenhouse aquaponic farming system that integrates aquaculture and hydroponic cultivation based on the Internet of Things (IoT). The proposed integration method employs multi-modal sensors and LED-grow-lights as supporting technologies to enable remote monitoring and control of aquaponic farming conditions through the Blynk IoT platform. The multi-modal sensors utilized in this research include DHT11 for monitoring air temperature and humidity, light dependent resistor (LDR) and infrared (IR) sensors for measuring sunlight intensity and LED-grow-lights levels, a soil moisture sensor for measuring hydroponic water volume, DS18B20 for monitoring aquaponic water temperature, a total dissolved solids (TDS) sensor for nutrient concentration, and pH-4502C for measuring water acidity. The LED-grow-lights functions as an artificial light source to replace sunlight under unfavorable weather conditions. In this study, a Raspberry Pi was implemented as the central data processing unit, while the Blynk IoT platform was employed to transmit aquaponic greenhouse data to the farmer’s smartphone. The experimental results demonstrate that the integration of multi-modal sensors enables effective monitoring of IoT-based aquaponic farming conditions with an accuracy level of up to 94% compared with other product of sensors, a monitoring and control delay ranging transmits the data from the embedded devices to smartphone farmer between 5 and 9 seconds, and reliable replacement of sunlight by the LED-grow-lights during adverse weather conditions.
Co-Authors Abdul Jalil Haerani, nining Irmawaty Iskandar Pujianti Wahyuningsih Suwatri Jura