FL-ToLeD: An Improved Lightweight Attention Convolutional Neural Network Model for Tomato Leaf Diseases Classification for Low-End Devices-Reference-Cited by-同舟云学术

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FL-ToLeD: An Improved Lightweight Attention Convolutional Neural Network Model for Tomato Leaf Diseases Classification for Low-End Devices

Published:2024 Issue: Volume:12 Page:73561-73580
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Alnamoly Mahmoud H.¹^ORCID,Hady Anar A.²^ORCID,Abd El-Kader Sherine M.³^ORCID,El-Henawy Ibrahim¹^ORCID

Affiliation:

1. Department of Computer Science, Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt

2. Faculty of Informatics and Computer Sciences, The British University in Egypt (BUE), El Shorouk City, Cairo, Egypt

3. Computers and Systems Department, Electronics Research Institute, Cairo, Egypt

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10538171.pdf?arnumber=10538171

Reference51 articles.

1. Morphological and Molecular Characterization of Large-spored Alternaria Species Associated with Potato and Tomato Early Blight in Egypt

2. A Review of the Most Common and Economically Important Diseases That Undermine the Cultivation of Tomato Crop in the Mediterranean Basin

3. Disease detection of Cercospora Leaf Spot in sugar beet by robust template matching

4. A Disease Index for Efficiently Detecting Wheat Fusarium Head Blight Using Sentinel-2 Multispectral Imagery

5. Tomato yellow leaf curl virus, an emerging virus complex causing epidemics worldwide

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