Evaluating the Performance of YOLO Object Detectors for Plant Disease Detection-Reference-Cited by-同舟云学术

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Evaluating the Performance of YOLO Object Detectors for Plant Disease Detection

Published:2024-07-23 Issue: Volume: Page:1-6
ISSN:
Container-title:2024 11th International Conference on Wireless Networks and Mobile Communications (WINCOM)
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Author:

Natij Youssef¹,El Karch Hajar²,Maafiri Ayyad³,Mezouari Abdelkader¹

Affiliation:

1. Ibn Tofail University,Laboratory of Electrical Engineering and Energy Systems, Faculty Of Sciences,Kenitra,Morocco

2. National School Of Applied Sciences, Ibn Tofail University,Laboratory of Advanced Systems Engineering,Kenitra,Morocco

3. National School Of Applied Sciences (ENSA), Ibn Tofail University,Engineering Science Laboratory,Kenitra,Morocco

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10654795/10654813/10656918.pdf?arnumber=10656918

Reference19 articles.

1. Threats to global food security from emerging fungal and oomycete crop pathogens

2. Correction for Ristaino et al., The persistent threat of emerging plant disease pandemics to global food security

3. Recent advances in image processing techniques for automated leaf pest and disease recognition – A review

4. Plant Disease Detection and Classification by Deep Learning—A Review

5. Object detection using YOLO: challenges, architectural successors, datasets and applications

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