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http://dx.doi.org/10.30693/SMJ.2022.11.4.9

A System for Determining the Growth Stage of Fruit Tree Using a Deep Learning-Based Object Detection Model  

Bang, Ji-Hyeon (순천대학교 스마트융합학부)
Park, Jun (순천대학교 스마트융합학부)
Park, Sung-Wook (순천대학교 스마트융합학부)
Kim, Jun-Yung (순천대학교 스마트융합학부)
Jung, Se-Hoon (안동대학교 창의융합학부)
Sim, Chun-Bo (순천대학교 인공지능학부)
Publication Information
Smart Media Journal / v.11, no.4, 2022 , pp. 9-18 More about this Journal
Abstract
Recently, research and system using AI is rapidly increasing in various fields. Smart farm using artificial intelligence and information communication technology is also being studied in agriculture. In addition, data-based precision agriculture is being commercialized by convergence various advanced technology such as autonomous driving, satellites, and big data. In Korea, the number of commercialization cases of facility agriculture among smart agriculture is increasing. However, research and investment are being biased in the field of facility agriculture. The gap between research and investment in facility agriculture and open-air agriculture continues to increase. The fields of fruit trees and plant factories have low research and investment. There is a problem that the big data collection and utilization system is insufficient. In this paper, we are proposed the system for determining the fruit tree growth stage using a deep learning-based object detection model. The system was proposed as a hybrid app for use in agricultural sites. In addition, we are implemented an object detection function for the fruit tree growth stage determine.
Keywords
Deep learning; Computer Vision; Object Detection; YOLO; Mobile system;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
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