TY - GEN
T1 - Identification of Cotton Leaf Curl Disease Using CNN and Vision Transformer
AU - Biju, Goel
AU - Khan, Asiya
AU - Walker, David
AU - Qadri, Salman
AU - Hssan, Qaim
AU - Mahmood, Khalid
AU - Hanan, Abdul
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Cotton is one of the most widely cultivated crops in the world, with a large proportion grown in developing countries. For better cotton management and yield, deep learning techniques are developed in this work. Therefore, the aim of this paper is twofold: first to create an open-source dataset of healthy and diseased cotton leaves (leaf curl virus-affected). A new custom dataset and the training/validation/testing sets and the raw dataset themselves have been provided in the GitHub repository. Secondly, to develop image classification models based on Convolution Neural Networks (CNNs) through an initial baseline model and Vision Transformer (ViT) of the cotton leaves. It shows how the vanilla model for a vision transformer with the addition of existing algorithms such as shifted patch tokenisation and locality self-attention can be used in this context to give over 80% accuracy on an unseen testing dataset. Facebook Research's ConViT hybrid model with GPSA layers is also evaluated in this context, using the automatic and manual implementation from code, and has shown the “convit-base” model providing nearly 85% accuracy and better generalisation over the epochs of training than the CNN baselines and the ViT model.
AB - Cotton is one of the most widely cultivated crops in the world, with a large proportion grown in developing countries. For better cotton management and yield, deep learning techniques are developed in this work. Therefore, the aim of this paper is twofold: first to create an open-source dataset of healthy and diseased cotton leaves (leaf curl virus-affected). A new custom dataset and the training/validation/testing sets and the raw dataset themselves have been provided in the GitHub repository. Secondly, to develop image classification models based on Convolution Neural Networks (CNNs) through an initial baseline model and Vision Transformer (ViT) of the cotton leaves. It shows how the vanilla model for a vision transformer with the addition of existing algorithms such as shifted patch tokenisation and locality self-attention can be used in this context to give over 80% accuracy on an unseen testing dataset. Facebook Research's ConViT hybrid model with GPSA layers is also evaluated in this context, using the automatic and manual implementation from code, and has shown the “convit-base” model providing nearly 85% accuracy and better generalisation over the epochs of training than the CNN baselines and the ViT model.
KW - Convolutional Neural Network
KW - Cotton Leaf Curl
KW - Vision Transformer
UR - http://www.scopus.com/inward/record.url?scp=85192211204&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-47724-9_44
DO - 10.1007/978-3-031-47724-9_44
M3 - Conference proceedings published in a book
AN - SCOPUS:85192211204
SN - 9783031477232
T3 - Lecture Notes in Networks and Systems
SP - 670
EP - 688
BT - Intelligent Systems and Applications - Proceedings of the 2023 Intelligent Systems Conference IntelliSys Volume 2
A2 - Arai, Kohei
PB - Springer Science and Business Media Deutschland GmbH
T2 - Intelligent Systems Conference, IntelliSys 2023
Y2 - 7 September 2023 through 8 September 2023
ER -