Implementasi sistem penyemprotan herbisida cerdas berbasis wireless sensor network untuk pertanian presisi

Nailul Muna; Norma Ningsih; Nanang Syahroni; Ekananda Sulistyo Putra; Muhammad Naufal Asyam Muflih; Muhamad Nur Afriza Farkhan

doi:10.33795/eltek.v24i1.9865

Authors

Nailul Muna Politeknik Elektronika Negeri Surabaya
Norma Ningsih
Nanang Syahroni
Ekananda Sulistyo Putra
Muhammad Naufal Asyam Muflih
Muhamad Nur Afriza Farkhan

DOI:

https://doi.org/10.33795/eltek.v24i1.9865

Keywords:

IoT, Pertanian Presisi, Raspberry Pi, Smart Spraying, Wireless Sensor Network

Abstract

Penelitian ini mengembangkan sistem penyemprotan herbisida otomatis berbasis Internet of Things (IoT) dan Wireless Sensor Network (WSN) untuk mendukung pertanian presisi. Sistem terdiri atas dua node penyemprotan yang dilengkapi Raspberry Pi 5, ESP32, IP Camera, sensor ultrasonik, sensor waterflow, relay, dan pompa penyemprot. Raspberry Pi digunakan sebagai edge processing unit, sedangkan ESP32 berfungsi sebagai pengendali sensor dan aktuator. Sistem menggunakan SSD-MobileNet-V2 FPNLite untuk mendeteksi keberadaan gulma sebagai mekanisme pendukung pengambilan keputusan penyemprotan otomatis. Seluruh data monitoring dikirimkan ke Firebase dan divisualisasikan melalui website secara real-time. Hasil pengujian menunjukkan bahwa rata-rata akurasi sensor ultrasonik setelah kalibrasi mencapai 97,53% pada Node 1 dan 97,59% pada Node 2, sedangkan sensor waterflow mencapai 95,94% pada Node 1 dan 97,43% pada Node 2. Pengujian komunikasi data menunjukkan rata-rata latency sebesar 1,9 detik. Selain itu, seluruh perangkat keras dan website monitoring berhasil beroperasi dengan baik selama pengujian sistem. Hasil penelitian menunjukkan bahwa sistem mampu mengintegrasikan proses monitoring, komunikasi data, dan penyemprotan herbisida secara otomatis.

ABSTRACT

An automatic herbicide spraying hardware system based on the Internet of Things (IoT) and Wireless Sensor Network (WSN) was developed to support precision agriculture. The system consists of two spraying nodes equipped with Raspberry Pi 5, ESP32, IP Camera, ultrasonic sensor, waterflow sensor, relay, and spraying pump. Raspberry Pi functions as the edge processing unit, while ESP32 serves as the sensor and actuator controller. The system utilizes SSD-MobileNet-V2 FPNLite to detect weeds as a supporting mechanism for automatic spraying decision-making. All monitoring data are transmitted to Firebase and visualized through a real-time monitoring website. Experimental results show that the average error of the calibrated ultrasonic sensor reached 2.47% on Node 1 and 2.41% on Node 2, while the waterflow sensor achieved 4.06% on Node 1 and 2.57% on Node 2. Data communication testing showed an average latency of 1.9 seconds. In addition, all hardware components and the monitoring website operated properly during system testing. The experimental results demonstrate that the developed system is capable of integrating monitoring, data communication, and automatic herbicide spraying processes.

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