Gema Parasti Mindara, Lathifunnisa Fathonah, Inna Novianty, Afifah Rodhiyatun Nisa, Muhammad Arif Bagus Dewanto, Herlambang Nurasyid Ramadhan, Annaliah Fahlevy

Quackbot: Robot Pengumpul Telur Bebek Menggunakan Artificial Intelligence Berbasis IoT

Introduction

Quackbot: robot pengumpul telur bebek menggunakan artificial intelligence berbasis iot. Quackbot: Robot AI & IoT pengumpul telur bebek otomatis, mengatasi tantangan pengumpulan manual. Deteksi telur akurat dengan YOLOv8, monitoring real-time, tingkatkan efisiensi dan transparansi peternakan.

Abstract

The rapid advancement of robotic technology in the digital era has significantly impacted various industries, including poultry farming. One of the challenges faced by farmers is the manual collection of eggs, which poses risks of breakage and potential fraud in egg count records by workers. To address this issue, Quackbot, an AI and Internet of Things (IoT)-based egg-fetching robot, has been developed. This robot integrates YOLOv8 as an AI model to accurately detect eggs and an IoT system that allows farmers to monitor the number of collected eggs in real-time. The development of this robot follows the HDLC method, which includes Planning, Analysis, Design, Implementation, and Maintenance. The robot is designed to move autonomously to collect eggs and return to its basecamp once it reaches maximum capacity. This system enhances efficiency, safety, and transparency in egg collection, reducing losses and increasing farm productivity.

Review

The paper introduces "Quackbot," an innovative AI and IoT-based robot designed for automated duck egg collection, a critical and challenging task in poultry farming. This work addresses significant issues such as egg breakage, manual labor risks, and potential data inaccuracies, which are common pain points for farmers. By proposing an intelligent solution that integrates YOLOv8 for accurate egg detection and an IoT system for real-time monitoring, the authors present a highly relevant and potentially transformative approach to enhancing efficiency and transparency in agricultural practices. The concept is timely and directly responds to the growing need for automation in the agricultural sector, offering a promising solution to modernize traditional farming. A key strength of the proposed Quackbot lies in its sophisticated technological integration. The utilization of YOLOv8, a state-of-the-art object detection model, promises high accuracy in identifying eggs, which is crucial for successful collection in potentially varied farm environments. Coupled with an IoT system, farmers gain invaluable real-time insights into egg counts, significantly reducing the potential for fraud and improving record-keeping and inventory management. The adherence to the HDLC development methodology indicates a structured and systematic approach to the robot's design and implementation, suggesting a robust foundation for the system's development and long-term maintenance. The autonomous movement capability further underscores its potential to streamline operations and minimize human intervention, thereby enhancing safety and overall farm productivity. While the abstract presents a compelling vision, a comprehensive full paper would greatly benefit from additional empirical data and detailed analyses. Specifically, quantitative results on the performance metrics of Quackbot are crucial, such as the egg detection accuracy of YOLOv8 under diverse lighting and environmental conditions, the robot's collection efficiency rate, operational speed, battery life, and overall energy consumption. Furthermore, a comparative analysis demonstrating the tangible benefits against traditional manual collection methods (e.g., reduction in breakage percentage, time saved, cost savings) would strengthen the claims of increased efficiency and productivity. Discussions on scalability for larger farm operations, robustness in challenging terrain, and the potential impact on duck welfare during collection would also add significant value to this promising research.

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