Advancing Weed Detection in Agricultural Landscapes Using Computer Vision-Reference-Cited by-同舟云学术

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Advancing Weed Detection in Agricultural Landscapes Using Computer Vision

Published:2023-09-12 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)
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Author:

Tan Bei Ren¹,Moung Ervin²,Afnan Uda Muhd. Nur³,Kin Teo Kenneth Tze⁴,Angeline Lorita⁴

Affiliation:

1. School of Computer Science, Taylor's University,Subang Jaya,Malaysia

2. Universiti Malaysia Sabah,Faculty of Computing and Informatics,Kota Kinabalu,Malaysia

3. Universiti Malaysia Sabah,Faculty of Engineering,Kota Kinabalu,Malaysia

4. Universiti Malaysia Sabah,Modelling, Simulation & Computing Laboratory, Faculty of Engineering,Kota Kinabalu,Malaysia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10291172/10291205/10291809.pdf?arnumber=10291809

Reference26 articles.

1. Real time detection of inter-row ryegrass in wheat farms using deep learning

2. Real-time classification of weeds in organic carrot production using deep learning algorithms

3. Performance of deep learning models for classifying and detecting common weeds in corn and soybean production systems

4. Bonnet: An open-source training and deployment framework for semantic segmentation in robotics using CNNs;milioto;ArXiv,2018

5. DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning

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