E2ETCA: End-to-end training of CNN and attention ensembles for rice disease diagnosis

doi:10.1016/j.jia.2024.03.075

Journal of Integrative Agriculture

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Md. Zasim Uddin^1#, Md. Nadim Mahamood¹, Ausrukona Ray¹, Md. Ileas Pramanik¹, Fady Alnajjar^2#, 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

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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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