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

2026, Vol. 25

Issue (2): 756-768 DOI: 10.1016/j.jia.2024.03.075

Plant Protection

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

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 5404 , Bangladesh

²Department of Computer Science and Software Engineering, United Arab Emirates University, Alain 15551, UAE

³Department of Computer Science and Digital Technologies, University of East London, London E16 2RD, UK

Highlights
● We introduce an end-to-end ensemble framework that fuses features from Inception V3, DenseNet-201, and a vision transformer to improve robustness and generalizability in rice disease diagnosis.

Abstract
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Abstract

Rice is one of the most important staple crops globally. Rice plant diseases can severely reduce crop yields and, in extreme cases, lead to total production loss. Early diagnosis enables timely intervention, mitigates disease severity, supports effective treatment strategies, and reduces reliance on excessive pesticide use. Traditional machine learning approaches have been applied for automated rice disease diagnosis; however, these methods depend heavily on manual image preprocessing and handcrafted feature extraction, which are labor-intensive and time-consuming and often require domain expertise. Recently, end-to-end deep learning (DL) models have been introduced for this task, but they often lack robustness and generalizability across diverse datasets. To address these limitations, we propose a novel end-to-end training framework for convolutional neural network (CNN) and attention-based model ensembles (E2ETCA). This framework integrates features from two state-of-the-art (SOTA) CNN models, Inception V3 and DenseNet-201, and an attention-based vision transformer (ViT) model. The fused features are passed through an additional fully connected layer with softmax activation for final classification. The entire process is trained end-to-end, enhancing its suitability for real-world deployment. Furthermore, we extract and analyze the learned features using a support vector machine (SVM), a traditional machine learning classifier, to provide comparative insights. We evaluate the proposed E2ETCA framework 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 combined version of all three. Using standard evaluation metrics (accuracy, precision, recall, and F1-score), our framework demonstrates superior performance compared to existing SOTA methods in rice disease diagnosis, with potential applicability to other agricultural disease detection tasks.

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

Received: 23 October 2023 Accepted: 16 January 2024 Online: 27 March 2024

Fund: We are grateful to the Begum Rokeya University, Rangpur, and the United Arab Emirates University, UAE 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. 2026. E2ETCA: End-to-end training of CNN and attention ensembles for rice disease diagnosis. Journal of Integrative Agriculture, 25(2): 756-768.

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