Skripsi

PENGEMBANGAN SISTEM PENYORTIRAN OTOMATIS UNTUK BUAH MENGGUNAKAN PENGENDALI LENGAN ROBOT

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Penilaian

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Increasing productivity in the agricultural industry, especially in the fruit sorting process, requires an effective and accurate automated system. This research develops an automatic sorting system for small fruits, especially achacha fruits, by utilizing deep learning technology based on YOLO (You Only Look Once) and Epson T3 robot arm controller. The system is designed to detect and classify fruits based on their ripeness level and physical condition in real-time. The image dataset of achacha fruit is collected using a Basler camera integrated with Pylon Viewer software, then labeled through the Roboflow platform and trained using Google Colab to utilize the capabilities of the GPU in accelerating the training process of the YOLOv8 model. In this research, the robot arm is controlled using LabVIEW software which functions to direct the end-effector in the sorting process based on the detection results of the deep learning model. The integration process between Python and LabVIEW is done through Python Node to ensure efficient communication between the computer vision system and hardware. The developed system successfully improved the sorting speed and accuracy compared to the conventional manual method. This research makes a significant contribution in the application of computer vision technology and robotics for automation in the agricultural sector. In addition, the method used can be applied to various types of fruits with minimal modifications to the dataset and model parameters. Thus, this system is expected to be an innovative solution in the harvest sorting process in the future, especially in supporting the concept of smart agriculture.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507000369	T164806	T1648062024	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1648062024

Publisher

Indralaya : Prodi Teknik Mesin, Fakultas Teknik Universitas Sriwijaya., 2024

Collation

xxvii, 90 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

629.807

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Teknik kontrol otomatis
Prodi Teknik Mesin, Fakultas Teknik

Specific Detail Info

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Statement of Responsibility

MI

Other version/related

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