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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmiah Informatika Komputer Jurnal Ilmiah Teknologi dan Rekayasa Prosiding KOMMIT Majalah Ilmiah Matematika Komputer Techno.Com: Jurnal Teknologi Informasi Jurnal Ilmiah KOMPUTASI JURNAL PERTANIAN PRESISI (JOURNAL OF PRECISION AGRICULTURE) INTECOMS: Journal of Information Technology and Computer Science Teknika Jurnal Pengabdian Kepada Masyarakat Darma Saskara Advance Sustainable Science, Engineering and Technology (ASSET)
Nur Sultan Salahuddin
Program Studi Sistem Komputer, Fakultas Ilmu Komputer Dan Teknologi Informatika Universitas Gunadarma
Agriculture, Biological Sciences & Forestry Automotive Engineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Social Sciences

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Journal : Advance Sustainable Science, Engineering and Technology (ASSET)

 1 
Development of a Robotic System for Agricultural Pest Detection: A Case Study on Chili Plants Nur Sultan Salahuddin; Fathi Muthia Tarie; Trini Saptariani
Advance Sustainable Science Engineering and Technology Vol. 7 No. 1 (2025): November-January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i1.1152

Abstract

Chili peppers, a key agricultural commodity in Indonesia, are highly susceptible to pest infestations and diseases, leading to significant economic losses and challenges in sustainable farming. This study presents the design and implementation of a real-time pest detection system that integrates robotics, computer vision, and deep learning to enhance agricultural productivity. The system is built on a Raspberry Pi 5 and Arduino Mega Pro Mini, utilizing a camera for image capture and ultrasonic sensors for navigation. A ResNet-based model was trained on a dataset of 2,703 chili leaf images, categorized into healthy and diseased classes, achieving a detection accuracy of  91%. The system provides early warnings to farmers through a web-based interface, allowing timely intervention and reducing reliance on chemical pesticides. While promising, the system faced challenges such as environmental variability, which influenced image recognition accuracy. By automating pest detection and promoting precision farming, this innovation addresses the need for sustainable agricultural practices, contributing to global food security and reducing environmental impact.
Co-Authors A.A. Ketut Agung Cahyawan W AA Sudharmawan, AA Ahmad S S Zambarkah Ahmad S S Zambarkah Aisyah Aisyah Alvien Yuliant Anacostiana Kowanda Aviana Furi Busono Soerowirdjo Darmawan, Andrea Dhoni Setyanto Fahnun, Budi Utami Fathi Muthia Tarie Fauziah Fauziah Febrin Ludia Ramadini Haiban, Marastrika Farhan Nur Hustinawaty Hustinawaty, Hustinawaty Karjadi, Mochamad Mohammad Iqbal Musyaffa, Aqilla Rahman Nur Iramadhan Poernomo Sari, Raden Roro Sri Qisthi Al Hazmi Hidayatur Rohman Rafi, Farid Al Sandy S. Prayogo Sari, RR. Sri Poernomo Sari, Sri Peornomo Sari, Sri Purnomo Sri Poernomo Sari Sri Poernomo Sari Sri Poernomo Sari Sri Poernomo Sari Suger, Herik Sulardi . Supiani Supiani, Supiani Supriyono Supriyono Sutiawan Tresno Trini Saptariani Trini Saptariani