• Journal of Internet of Things and Convergence
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• v.6 no.1
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• pp.83-95
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• 2020

Being a dense and complex task of precisely detecting the weeds in practical crop field environment, previous approaches lack in terms of speed of processing image frames with accuracy. Although much of the attention has been given to classify the plants diseases but detecting crop weed issue remained in limelight. Previous approaches report to use fast algorithms but inference time is not even closer to real time, making them impractical solutions to be used in uncontrolled conditions. Therefore, we propose a detection model for the complex rice weed detection task. Experimental results show that inference time in our approach is reduced with a significant margin in weed detection task, making it practically deployable application in real conditions. The samples are collected at two different growth stages of rice and annotated manually

• Journal of Biosystems Engineering
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• v.27 no.2
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• pp.143-150
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• 2002

Several researches for site-specific weed control have tried to increase accuracy of weed detection with machine vision technique. However, there is a problem which needs substantial time to perform site-specific spraying. Therefore, new technology for real-time precision spraying system is needed. This research was executed to develope the new technology to estimate weed density and size in real time, and to conduct a real-time site-specific spraying. It would effectively reduce herbicide amounts applied for a crop field. The real-time precision spraying system consisted of a Differential Global Positioning System (DGPS) with an error of 2 cm, a machine vision system, a geomagnetic sensor for correction of view point of CCD camera and an automatic sprayer with separately controlled nozzle. The weed density was calculated with comparison between position information and a pre-designed electronic map. The position information was obtained in real time using the DGPS and the machine vision. The electronic map contained a position database of crops automatically constructed when seeding. The developed system was tested on an experimental field of Seoul National University. Success rate of the spraying was about 61%.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.2
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• pp.73-81
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• 2024

Recently, mobile manipulators have been utilized in agriculture industry for weed removal and harvest automation. This paper proposes a domain adaptive fruit detection method for harvest automation, by utilizing OWL-ViT model which is an open-vocabulary object detection model. The vision-language model can detect objects based on text prompt, and therefore, it can be extended to detect objects of undefined categories. In the development of deep learning models for real-world problems, constructing a large-scale labeled dataset is a time-consuming task and heavily relies on human effort. To reduce the labor-intensive workload, we utilized a large-scale public dataset as a source domain data and employed a domain adaptation method. Adversarial learning was conducted between a domain discriminator and feature extractor to reduce the gap between the distribution of feature vectors from the source domain and our target domain data. We collected a target domain dataset in a real-like environment and conducted experiments to demonstrate the effectiveness of the proposed method. In experiments, the domain adaptation method improved the AP50 metric from 38.88% to 78.59% for detecting objects within the range of 2m, and we achieved 81.7% of manipulation success rate.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.10
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• pp.7176-7181
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• 2015

This thesis is about the system where the solar module is attached to the high functional guardrail posts with anti-weed, anti-plant, and road-kill applied to produce internal power, enabling the integrated control and real-time monitoring of appearance of wildlife and road conditions using the USN. The whole system consists of a photovoltaic module(PV), a detection sensor(pyroelectric), a controller(operation select and motion sensor), the USN system, the DB(sound and flash), an output unit of sound and flash, and the control system of road-kill prevention and safety induction for vehicles. Thus this study aims to address the remote control and monitoring system of multipurpose guardrails to improve road environment, prevent road-kills, protect wild animals, and guide cars safely by using the USN which is combined with new renewable energy and IT convergence technology. As a result of the study on the remote control and monitoring system using the USN, it was ascertained that the response time of the unmanned sensing system was within 5.1 ms with the current consumption of 0.328 mA, and the data transmission speed of the remote control system was 250 kbps with the current consumption of 0.283 mA.