HRT-AD Net: Hybrid Residual Transformer Attention-based Deep Network for Rice Disease Diagnosis

SUSHANTA KUMAR MOHANTY; ABHA MAHALWAR; CHANDRAKANT MALLICK; Sidhartha Sankar Dora

HRT-AD Net: Hybrid Residual Transformer Attention-based Deep Network for Rice Disease Diagnosis

Author(s)	Mr. SUSHANTA KUMAR MOHANTY, Dr. ABHA MAHALWAR, Dr. CHANDRAKANT MALLICK, Dr. Sidhartha Sankar Dora
Country	India
Abstract	One of the most significant staple crops that are consumed globally is rice (Oryaza sativa), and to avoid losses in its production and to maintain food security, it is necessary to timely identify diseases on its leaves. Nevertheless, disease detection in the real-field setting is not easy owing to the complication of the background, fluctuation of the light, inter-class similarity and the tiny size of the lesion areas. In this paper, the Hybrid CNN-Transformer Attention Network (HRT-ADNet) will be proposed to achieve robust detection and classification of rice disease. The suggested model combines a lightweight convolutional neural network backbone that localizes textures and a transformer encoder that models global features. A dual attention model that includes channel and spatial attention is proposed to improve lesion-conscious feature refinement. Besides, a multi-task branch of segmentation is optional and is used to enhance interpretability and localization performance. The model is trained and tested on a four-class rice disease dataset containing: Brown Spot, Bacterial Leaf Blight, Rice Blast and Leaf Smut. The model proves to have an experimental result of 96.8% test accuracy and a macro-average AUC of 0.975, which is lower than traditional CNN and standalone transformer models. The lightweight architecture guarantees a lower level of computational complexity and feasibility of deployment in real time. The suggested framework offers a solution that is reliable, interpretable, and effective in the diagnosis of intelligent rice disease in the context of precision agriculture.
Keywords	Rice disease detection; Hybrid deep learning; Convolutional Neural Network (CNN); Vision Transformer; Dual attention mechanism; multi-task learning; Lesion segmentation; Precision agriculture
Field	Computer Applications
Published In	Volume 7, Issue 2, March-April 2026
Published On	2026-03-19

View / Download PDF File

E-ISSN 3048-7641

doi

CrossRef DOI is assigned to each research paper published in our journal.

AIJFR DOI prefix is
10.63363/aijfr

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 7 Isu 1 Cover Page Vol 6 Isu 6 Cover Page Vol 6 Isu 5

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About AIJFR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9898-79-39-59	editor@aijfr.com

Advanced International Journal for Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

HRT-AD Net: Hybrid Residual Transformer Attention-based Deep Network for Rice Disease Diagnosis

Share this