Improving Wheat Leaf Disease Classification: Evaluating Augmentation Strategies and CNN-Based Models With Limited Dataset-Reference-Cited by-同舟云学术

Improving Wheat Leaf Disease Classification: Evaluating Augmentation Strategies and CNN-Based Models With Limited Dataset

Published:2024 Issue: Volume:12 Page:69853-69874
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Ramadan Syed Taha Yeasin¹,Sakib Tanjim¹,Farid Fahmid Al²^ORCID,Islam Md. Shofiqul¹^ORCID,Abdullah Junaidi Bin³^ORCID,Bhuiyan Md Roman⁴^ORCID,Mansor Sarina²^ORCID,Karim Hezerul Bin Abdul²^ORCID

Affiliation:

1. Department of Computer Science and Engineering, Military Institute of Science and Technology (MIST), Dhaka, Bangladesh

2. Faculty of Engineering, Multimedia University, Cyberjaya, Malaysia

3. Faculty of Computing and Informatics, Multimedia University (MMU), Cyberjaya, Malaysia

4. Faculty of Computer Science and Informatics, Berlin School of Business and Innovation (BSBI), Wolverhampton, U.K.

Funder

Multimedia University, Malaysia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10521470.pdf?arnumber=10521470

Reference28 articles.

1. World Food and Agriculture - Statistical Pocketbook 2021

2. Crop losses due to diseases and their implications for global food production losses and food security

3. Multimodal Hybrid Deep Learning Approach to Detect Tomato Leaf Disease Using Attention Based Dilated Convolution Feature Extractor with Logistic Regression Classification

4. Deep Dilated Convolutional Neural Network for Crowd Density Image Classification with Dataset Augmentation for Hajj Pilgrimage

5. Handwritten character recognition using convolutional neural network

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