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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) Sistemasi: Jurnal Sistem Informasi
Iskandar, Irmawaty
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Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering

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Multi-modal sensor integration in chicken-fish-vegetable greenhouse agriculture based on internet of things Risal, Muhammad; Wahyuningsih, Pujianti; Jura, Suwatri; Iskandar, Irmawaty; Jalil, Abdul
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.
Integrated Chicken–Fish–Vegetable Farming based on a Multi-Modal Sensor System using ESP32 and Raspberry Pi Risal, Muhammad; Wahyuningsih, Pujianti; Jura, Suwatri; Iskandar, Irmawaty; Masiku, Satria Tandi Allo; Salam, Muh Yunus; Jalil, Abdul
Sistemasi: Jurnal Sistem Informasi Vol 15, No 4 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i4.5662

Abstract

This study aims to develop an integrated Chicken–Fish–Vegetable (AISa) farming system based on a multi-modal sensor system controlled by ESP32 and Raspberry Pi. In this system, the ESP32 functions as a microcontroller responsible for reading multi-modal sensor inputs in the form of analog data, converting them into digital signals, and transmitting the data to the Raspberry Pi via a serial communication protocol. The Raspberry Pi, in turn, processes the digital sensor data and manages overall control operations within the AISa farming system. The multi-modal sensors used in this study include a camera for farm monitoring, a water level sensor for detecting water height, a DHT11 sensor for air temperature and humidity, an LDR for light intensity detection, an infrared sensor for sunlight detection, a TDS sensor for measuring water nutrient levels, a pH sensor for detecting water acidity, and a DS18B20 sensor for monitoring fish pond temperature. The method applied in this study for converting analog sensor data involves utilizing the GPIO pins of the ESP32 to perform analog-to-digital conversion, while the Raspberry Pi is employed to process the AISa farming data due to its computational capabilities similar to a computer. The results indicate that the multi-modal sensor system is capable of providing comprehensive environmental information about the AISa farming conditions. The data are initially captured by the ESP32 in analog form, converted into digital data, and then transmitted to the Raspberry Pi for further processing. In addition, this study establishes a data design framework for future research involving the integration of AISa greenhouse farming systems with Internet of Things (IoT) technologies.
Co-Authors Abdul jalil Jura, Suwatri Masiku, Satria Tandi Allo Muhammad Risal, Muhammad Salam, Muh Yunus Wahyuningsih, Pujianti