LoRa and IoT Based Monitoring System for Detecting Ganoderma Disease Attacks on Oil Palm Plants - Muhaimin, Ahmad Dzakiyuddin; Hadary, Ferry; Suswanto, Iman (Faculty of Engineering, Universitas Tanjungpura, 2023)
This research develops a monitoring system based on LoRa and IoT to detect ganoderma disease attacks on oil palm plants, aiming to detect ganoderma disease early. The method used involves the use of MQ-138 and TGS 2611 sensors to detect the level of volatile organic compounds gas emitted by palm trunks affected by ganoderma disease. The research results show that this system is capable of detecting ganoderma disease with a range of increased values on healthy palm plant samples: 0 - 5 values; moderate oil palm plants: 8 - 22 values; and on sick oil palm plants: 28 - 32 values. The system can also transmit data up to a range of 757.92 meters with an RSSI value of -105 dBm in conditions with minimal obstacles, and send and receive information from LoRa devices to Blynk IoT with an average time difference of 0.695 seconds for LoRa devices and 0.701 seconds for Blynk IoT.