Open Access

ARTICLE

Mango Disease Detection Using Fused Vision Transformer with ConvNeXt Architecture

Faten S. Alamri¹, Tariq Sadad^2,*, Ahmed S. Almasoud³, Raja Atif Aurangzeb⁴, Amjad Khan³

1 Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
2 Department of Computer Science, University of Engineering & Technology, Mardan, 23200, Pakistan
3 AIDA Lab, Department of Information Systems, College of Computer & Information Sciences, Prince Sultan University, Riyadh, 12435, Saudi Arabia
4 Department of Computer Science, International Islamic University, Islamabad, 44000, Pakistan

* Corresponding Author: Tariq Sadad. Email:

(This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)

Computers, Materials & Continua 2025, 83(1), 1023-1039. https://doi.org/10.32604/cmc.2025.061890

Received 05 December 2024; Accepted 11 February 2025; Issue published 26 March 2025

Abstract

Mango farming significantly contributes to the economy, particularly in developing countries. However, mango trees are susceptible to various diseases caused by fungi, viruses, and bacteria, and diagnosing these diseases at an early stage is crucial to prevent their spread, which can lead to substantial losses. The development of deep learning models for detecting crop diseases is an active area of research in smart agriculture. This study focuses on mango plant diseases and employs the ConvNeXt and Vision Transformer (ViT) architectures. Two datasets were used. The first, MangoLeafBD, contains data for mango leaf diseases such as anthracnose, bacterial canker, gall midge, and powdery mildew. The second, SenMangoFruitDDS, includes data for mango fruit diseases such as Alternaria, Anthracnose, Black Mould Rot, Healthy, and Stem and Rot. Both datasets were obtained from publicly available sources. The proposed model achieved an accuracy of 99.87% on the MangoLeafBD dataset and 98.40% on the MangoFruitDDS dataset. The results demonstrate that ConvNeXt and ViT models can effectively diagnose mango diseases, enabling farmers to identify these conditions more efficiently. The system contributes to increased mango production and minimizes economic losses by reducing the time and effort needed for manual diagnostics. Additionally, the proposed system is integrated into a mobile application that utilizes the model as a backend to detect mango diseases instantly.

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Keywords

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