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Journal of Integrative Agriculture
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E2ETCA: End-to-end training of CNN and attention ensembles for rice disease diagnosis
Md. Zasim Uddin1#, Md. Nadim Mahamood1, Ausrukona Ray1, Md. Ileas Pramanik1, Fady Alnajjar2#, Md Atiqur Rahman Ahad3

1Department of Computer Science and Engineering, Begum Rokeya University, Rangpur, Bangladesh 

2Department of Computer Science and Software Engineering, United Arab Emirates University, UAE 

3Department of Computer Science and Digital Technologies, University of East London, London, UK

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Abstract  Rice is one of the most important crops worldwide. Diseases of the rice plant can drastically reduce crop yield and even lead to complete loss of production. Early diagnosis can reduce the severity and help efforts to establish effective treatment and reduce the usage of pesticides. Traditional machine learning approaches have already been employed for automatic diagnosis. However, they heavily rely on manual preprocessing of images and handcrafted features, which is challenging, time-consuming, and may require domain expertise. Recently, a single end-to-end deep learning (DL)-based approach was employed to diagnose rice diseases. However, it is not highly robust, nor is it generalizable to every dataset. Hence, we propose a novel end-to-end training of convolutional neural network (CNN) and attention (E2ETCA) ensemble framework that fuses the features of two CNN-based state-of-the-art (SOTA) models along with those of an attention-based vision transformer model. These fused features are utilized for diagnosis by the addition of an extra fully connected layer with softmax. The whole procedure is performed end-to-end, which is very important for real-world applications. Additionally, we feed the extracted features into a traditional machine learning approach support vector machine for classification and further analysis. To verify the effectiveness of our proposed E2ETCA framework, we demonstrate it on three publicly available datasets: the Mendeley Rice Leaf Disease Image Samples dataset, the Kaggle Rice Diseases Image dataset, the Bangladesh Rice Research Institute dataset, and a combination of these three datasets. On the basis of various evaluation metrics (accuracy, precision, recall, and F1-score), our proposed  E2ETCA framework exhibits superior performance to existing SOTA approaches for rice disease diagnosis, which can also be generalizable in similar other domains.
Keywords:  Rice disease diagnosis       Ensemble method              CNN-based model              End-to-end model              Inception model              DenseNet model              Vision transformer model              Attention-based model              Support vector machine  
Online: 24 April 2024  
Fund: We are grateful to the Begum Rokeya University, Rangpur, and the United Arab Emirates University for partially supporting this work.
About author:  #Correspondence Md. Zasim Uddin, E-mail: zasim@brur.ac.bd; Fady Alnajjar, E-mail:fady.alnajjar@uaeu.ac.ae

Cite this article: 

Md. Zasim Uddin, Md. Nadim Mahamood, Ausrukona Ray, Md. Ileas Pramanik, Fady Alnajjar, Md Atiqur Rahman Ahad. 2024. E2ETCA: End-to-end training of CNN and attention ensembles for rice disease diagnosis. Journal of Integrative Agriculture, Doi:10.1016/j.jia.2024.03.075

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