Integrated Chicken–Fish–Vegetable Farming based on a Multi-Modal Sensor System using ESP32 and Raspberry Pi - Muhammad Risal; Pujianti Wahyuningsih; Suwatri Jura; Irmawaty Iskandar; Satria Tandi Allo Masiku; Muh Yunus Salam; Abdul Jalil (SISTEMASI, 2026)
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.