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All Journal Prosiding Semnastek JURNAL NASIONAL TEKNIK ELEKTRO Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Engineering Jurnal Hilirisasi IPTEKS (JHI) IJIES (International Journal of Innovation in Enterprise System) Jurnal Teknoif Teknik Informatika Institut Teknologi Padang CHIPSET (Journal on Computer Hardware, Signal Processing, Embedded System and Networking) Aceh International Journal of Science and Technology Jurnal Rekayasa elektrika
Hersyah, Mohammad Hafiz
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Health Professions Public Health Veterinary Other

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Identification and Prediction System of Tomato Leaf Diseases Based on YOLO and Random Forest Harzikra, Rafki; Hersyah, Mohammad Hafiz; Pramawahyudi, Pramawahyudi
CHIPSET Vol. 7 No. 02 (2026): Journal on Computer Hardware, Signal Processing, Embedded System and Networkin
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/chipset.7.02.126-131.2026

Abstract

Tomato crop failure in Indonesia caused by leaf disease can reach 78%, and manual visual monitoring by farmers is often delayed and subjective. This study designs and evaluates a portable, IoT-based diagnostic and prediction system for tomato leaf disease that can operate independently in the field. The system integrates a Raspberry Pi 4 single-board computer with a Raspberry Pi Camera V2, a DHT22 air temperature/humidity sensor, and a capacitive soil-moisture sensor. Two computational models run on-device: a YOLOv5 convolutional neural network that classifies leaf images into healthy, early blight, late blight, and septoria leaf spot; and a Random Forest classifier that predicts disease potential from air temperature, air humidity, and soil moisture. Results are transmitted via a 4G module to a Firebase cloud database and displayed on a real-time dashboard. Functional and field tests validated camera resolution, sensor accuracy, model accuracy, processing latency, and transmission delay. The YOLOv5-small model classified leaf images with 85.4% accuracy, while the Random Forest model predicted disease potential with 97% accuracy. On-device dual-model inference averaged 7.26 seconds and cloud transmission delay averaged 8.8 seconds, both within the 10-second design target. Field validation on a working tomato farm produced an average diagnostic correctness of 85.53%. The dual-model, off-grid-capable IoT system is technically feasible for real-time, field-deployable tomato leaf disease diagnosis and can help farmers act earlier on emerging infections, reducing potential crop loss.
Co-Authors - Andrizal Ahmad Nurul Azmi Anak Agung Gede Sugianthara Andrizal Andrizal Anisa Muhaimin Atillah Sridany Putri Budi Rahmadya Citra Berliana Derisma Derisma Dodon Yendri Eno Marozi Fezya Noviratama Firdaus Firdaus Firdaus Firdaus Firdaus Gentha Wijaya Hadelina, Rizka Hadining, Aulia Haditya Fajri Hamidatul Nesya Harzikra, Rafki Khairul Fikri Lathifah Arief, Lathifah Multri Okta Ilham, Randa Nela Fatma Nesya, Hamidatul Ni Luh Ari Yusasrini Novani, Nefy Puteri Ozil Afindra Putra PRAMAWAHYUDI, PRAMAWAHYUDI Purwoko, Agus Rahmad Fadhil Ratna Aisuwarya Revinessia Revinessia Revinessia, Revinessia Ririn Putri Anedya Savitri, Sekar Ayu Werman Kasoep Yan Heriyandi Yolanda, Desta Zaini Zaini Zaki Minango Dasman Zakky Aulia Zulfa, Bilqisthi Ananda