Multispectral Plant Disease Detection with Vision Transformer–Convolutional Neural Network Hybrid Approaches

De Silva, Malitha; Brown, Dane L

Title: Multispectral Plant Disease Detection with Vision Transformer–Convolutional Neural Network Hybrid Approaches
Creator: De Silva, Malitha
Creator: Brown, Dane L
Subject: To be catalogued
Date Issued: 2023
Date: 2023
Type: text
Type: article
Identifier: http://hdl.handle.net/10962/463428
Identifier: vital:76408
Identifier: xlink:href="https://doi.org/10.3390/s23208531"
Description: Plant diseases pose a critical threat to global agricultural productivity, demanding timely detection for effective crop yield management. Traditional methods for disease identification are laborious and require specialised expertise. Leveraging cutting-edge deep learning algorithms, this study explores innovative approaches to plant disease identification, combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to enhance accuracy. A multispectral dataset was meticulously collected to facilitate this research using six 50 mm filter filters, covering both the visible and several near-infrared (NIR) wavelengths. Among the models employed, ViT-B16 notably achieved the highest test accuracy, precision, recall, and F1 score across all filters, with averages of 83.3%, 90.1%, 90.75%, and 89.5%, respectively. Furthermore, a comparative analysis highlights the pivotal role of balanced datasets in selecting the appropriate wavelength and deep learning model for robust disease identification. These findings promise to advance crop disease management in real-world agricultural applications and contribute to global food security. The study underscores the significance of machine learning in transforming plant disease diagnostics and encourages further research in this field.
Format: computer
Format: online resource
Format: application/pdf
Format: 1 online resource (22 pages)
Format: pdf
Publisher: MDPI
Language: English
Relation: Sensors
Relation: De Silva, M. and Brown, D., 2023. Multispectral Plant Disease Detection with Vision Transformer–Convolutional Neural Network Hybrid Approaches. Sensors, 23(20), p.8531
Relation: Sensors volume 23 number 20 p. 8531 2023 1424-8220
Rights: Publisher
Rights: Use of this resource is governed by the terms and conditions of the MDPI Open Access Statement (https://www.int-res.com/journals/terms-of-use/)
Rights: Open Access

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