Using Yolo in detecting objects at Mixfarm Oelamasi | [Journal of Theoretical and Applied Information Technology • 2022]

Author(s):

1. ALBERT LAMBA: Information System Management Department, BINUS Graduate Program, Master of Information Systems Management, Bina Nusantara University, Jakarta 11480, Indonesia

2. TUGA MAURITSIUS, DRS., MT. PHD2: Information System Management Department, BINUS Graduate Program, Master of Information Systems Management, Bina Nusantara University, Jakarta 11480, Indonesia

Abstract:

Supervise farm field can be challenging. It takes too much time and effort just to explore the land. So, we suggest the farmer to start using Solar CCTV. Adding AI to detect activity on what happened in live recording monitoring easier. This paper discuss subtopic in artificial intelligence which is Convolutional Neural Network (CNN) to detect object on farm environment using YOLO. This research was carried out in a plantation located in Oelamasi, Kupang Regency, NTT with the aim of identifying object in the farm. This research purpose is to supervise ongoing process on the field. This research in conduct on papaya field with recorded CCTV video. This research used google collab as training platform, Yolov3 as reference model. The available GPU on google collab is Nvidia Tesla K80.We also predesign integrated system using cloud services so the data can be access via mobile apps or web browser. We hope from what we are doing can give others insight about the uses of artificial intelligence then can be applied on industries.

Page(s): 3750-3758

DOI: DOI not available

Published: Journal: Journal of Theoretical and Applied Information Technology, Volume: 100, Issue: 11, Year: 2022

Keywords:

CNN , YOLO , CCTV , cloud , Collab

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