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All Journal CHIPSET (Journal on Computer Hardware, Signal Processing, Embedded System and Networking)
Alhafiz Zhurrahman Mirza Utama
Universitas Andalas
Computer Science & IT Electrical & Electronics Engineering Engineering

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Sistem Pendeteksi Penyakit dan Pemantauan Kondisi Tanah serta Rekomendasi Pengendaliannya pada Tanaman Cabai Alhafiz Zhurrahman Mirza Utama; Rifki Suwandi; Lathifah Arief
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.7.02.132-139.2026

Abstract

The cultivation of chili plants faces challenges in early disease detection and accurate soil condition monitoring, which can potentially reduce crop yields and increase the risk of plant damage. This research aims to develop a disease detection and soil condition monitoring system based on Raspberry Pi, utilizing the You Only Look Once (YOLO) version 5 algorithm to detect diseases in chili plants from images captured by a camera. The system uses Raspberry Pi 4B as the data processing center, with a camera for capturing plant images and pH and soil moisture sensors to monitor soil conditions. The data obtained is used to provide better handling recommendations for farmers. Test results show that this system can detect plant diseases with 87.5% accuracy and an F1-score of 93.3% at a distance of 5 to 25 cm, although the accuracy decreases at distances greater than 25 cm. Furthermore, soil condition measurements provide important information about soil pH and moisture, helping farmers make more informed decisions in maintaining plant health. All disease detection and soil condition information is displayed on an LCD screen connected to the Raspberry Pi, making the system user-friendly for farmers. Therefore, this system is expected to be an effective solution for increasing crop yields and reducing losses due to diseases in chili plants, and it can be more broadly applied in the agricultural sector.
Co-Authors Lathifah Arief, Lathifah Rifki Suwandi