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All Journal Spektra: Jurnal Fisika dan Aplikasinya Elektrika Jurnal ICT : Information Communication & Technology Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Jurnal Abdi Masyarakat Indonesia PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Current STEAM and Education Research Journal of Applied Science, Technology & Humanities Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Fathonah, Lathifunnisa
Computer Engineering Technology, Vocational School, IPB University, Indonesia
Aerospace Engineering Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Public Health Social Sciences

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Journal : journal of applied science technology

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Quackbot Automated Robot Duck Egg Collector Based on Artificial Intelligence (AI) and Internet of Things (IoT) Analiah Fahlevy Yusuf; Muhammad Arif Bagus Dewanto; Herlambang Nurasyid Ramadhan; Dhiya Rizqi Bagus Wibowo; Antonio Banggas Gregory Sinaga; Anandito Daffa Wijayanto; Afifah Rodhiyatun Nisa4; Lathifunnisa Fathonah; Gema Parasti Mindara; Mohamad Fikih Amar Dani
Journal of Applied Science, Technology & Humanities | JASTH Vol. 2 No. 5 (2025): November 2025
Publisher : Batrisya Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62535/hsd3br02

Abstract

The development of robotics in various fields is advancing rapidly, especially in the current digital era. In the world of egg retrieval, there are specific procedures to prevent eggs from breaking during collection. Many farms still rely on manual egg retrieval, where workers collect eggs daily. However, there are concerns about dishonest workers who may steal eggs for personal gain, causing worry for farmers. To address this issue, an AI-based egg picker has been designed to act as a system controller and information manager. This system includes AI for egg detection and an IoT system for real-time monitoring, allowing farmers to track daily egg yields. The development of this automatic egg-picking robot involves several stages: data collection, data analysis, block diagrams, planning, needs analysis, device design, implementation, and maintenance. The tool utilizes AI YOLOv8 to detect eggs, which the robot then collects. Once the maximum capacity is reached, the robot returns to the base camp. This innovative approach not only improves efficiency and accuracy in egg collection but also ensures transparency and reduces the risk of theft, providing peace of mind for farmers and enhancing overall farm productivity.
MycoTrack: An Integrated Web and YOLOv5-Based Intelligent System for Monitoring and Predicting Wood Ear Mushroom Maturity Wuliddah Tamsil Barokah; Dwi Putra Kunto Anggoro; Nabil Kurnia Rozano; Ariel Mughnika Beers; Inna Novianty; Dodik Ariyanto; Lathifunnisa Fathonah
Journal of Applied Science, Technology & Humanities | JASTH Vol. 3 No. 2 (2026): March 2026
Publisher : Batrisya Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62535/kfkect89

Abstract

Wood ear mushroom (Auricularia auricula-judae) cultivation requires strict environmental control and accurate harvest monitoring. To overcome the shortcomings of labor-intensive and error-prone manual inspection, this research developed MycoTrack, an intelligent system integrating rail-based robotics, YOLOv5 computer vision, and IoT sensors. MycoTrack utilizes a rail-based robot powered by a Raspberry Pi 4. The robot carries a Pi Camera for visual data acquisition and DHT-22 sensors to measure environmental temperature and humidity. This environmental data is continuously monitored and transmitted to a web-based dashboard for real-time visualization, providing instantaneous decision support to farmers. The YOLOv5 model is specifically trained to detect three critical growth phases—incubation, pinning, and fruiting—which enables the prediction of optimal harvest timing. System validation showed DHT-22 sensor accuracy of 96.4% and the YOLOv5 model achieved a mAP@50 of 0.782 with inference speeds suitable for edge devices. The rail robot demonstrated minimal positional deviation (less than 2.3 cm). MycoTrack offers an accessible, automated solution, representing an advancement in precision agriculture for mushroom cultivation. The system is modularly designed for easy adaptation to other mushroom environments and species.
Co-Authors Aditya Wicaksono Adrian Fardan Andi Afifah Rodhiyatun Nisa4 Ahmad Fauzan Analiah Fahlevy Yusuf Anandito Daffa Wijayanto Angga Eben Ezer Antonio Banggas Gregory Sinaga Ariel Mughnika Beers Bayu Widodo Brilliant Sandynigy Fernando Daffa Ardyana Eka Putra Dea Ummul Khabibah Dewanto, Muhammad Arif Bagus Dhiya Rizqi Bagus Wibowo Dodik Ariyanto Dwi Putra Kunto Anggoro Edmund Banyu Kauripan Endang Purnama Giri Fahlevy, Annaliah Farhan Hakim Fauzi Ikhsan Suswanto Gema Parasti Gema Parasti Mindara Gema Parasti Mindara Gema Parasti Mindara Gema Parasti Mindara Herlambang Nurasyid Ramadhan Inna Novianty Inna Novianty Inna Novianty Inna Novianty Irmansyah Irmansyah Ka-sasi, R.I. Damai Laila Nur Azizah Liora Taya Marcelita, Faldiena Maulana Irfan Mindara, Gema Parasti Mohamad Fikih Amar Dani Muchammad Alifandino Satrio Muchlisinia, Newi Muhammad Arif Bagus Dewanto Muhammad Fadhil Al Faruq Muhammad Faza Elrahman Muhammad Hafizh Maulidan Muhammad Naufal Sutardi Muhammad Rafi Alexander Prayoga Nabil Arif A'isy Nabil Kurnia Rozano Naufal Rizqullah Firdaus Nisa, Afifah Rodhiyatun Novianty, Inna Qonita, Vellisya Afifa Radyanka Irza Pramono Ramadhan, Herlambang Nurasyid Raqhim Putra Al Rusdi Rheynesta Hannover Ridwan Siskandar Rio Ferddinansya Setiawan, Aep Shelvie Nidya Neyman Stefanny, Arlyn Sugiana, Lili Rahmawati Tiara Safitrah Walidatush Sholihah Wuliddah Tamsil Barokah Zolla Perdana Putra Harahap Zulvian Hardhan