Towards Real Time Detection of Rice Weed in Uncontrolled Crop Conditions

Muhammad Umraiz (무하마드 움라이즈); KIM, SANGCHEOL (김상철)

Towards Real Time Detection of Rice Weed in Uncontrolled Crop Conditions

Journal of Internet of Things and Convergence
Abbr : JKIOTS
2020, 6(1), pp.83-95
Publisher : The Korea Internet of Things Society
Research Area : Engineering > Computer Science > Internet Information Processing
Received : January 8, 2020
Accepted : February 17, 2020
Published : March 31, 2020

Muhammad Umraiz ¹, KIM, SANGCHEOL ¹

¹전북대학교

Candidate

ABSTRACT

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.

KEYWORDS

Real Time Weed Detection, Smart Farming using IoT, IoT with Object Detection, Rice Weed Detection, Weed Detection using Deep Learning

Citation status

* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

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Journal of Internet of Things and Convergence 2023 KCI Impact Factor : 0.89