Early Plant Disease Detection using Infrared and Mobile Photographs in Natural Environment
- De Silva, Malitha, Brown, Dane L
- Authors: De Silva, Malitha , Brown, Dane L
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464085 , vital:76474 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-031-37717-4_21"
- Description: Plant disease identification is a critical aspect of plant health management. Identifying plant diseases is challenging since they manifest themselves in various forms and tend to occur when the plant is still in its juvenile stage. Plant disease also has cascading effects on food security, livelihoods and the environment’s safety, so early detection is vital. This work demonstrates the effectiveness of mobile and multispectral images captured in viable and Near Infrared (NIR) ranges to identify plant diseases under realistic environmental conditions. The data sets were classified using popular CNN models Xception, DenseNet121 and ResNet50V2, resulting in greater than 92% training and 74% test accuracy for all the data collected using various Kolari vision lenses. Moreover, an openly available balanced data set was used to compare the effect of the data set balance and unbalanced characteristics on the classification accuracy. The result showed that balanced data sets do not impact the outcome.
- Full Text:
- Date Issued: 2023
- Authors: De Silva, Malitha , Brown, Dane L
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464085 , vital:76474 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-031-37717-4_21"
- Description: Plant disease identification is a critical aspect of plant health management. Identifying plant diseases is challenging since they manifest themselves in various forms and tend to occur when the plant is still in its juvenile stage. Plant disease also has cascading effects on food security, livelihoods and the environment’s safety, so early detection is vital. This work demonstrates the effectiveness of mobile and multispectral images captured in viable and Near Infrared (NIR) ranges to identify plant diseases under realistic environmental conditions. The data sets were classified using popular CNN models Xception, DenseNet121 and ResNet50V2, resulting in greater than 92% training and 74% test accuracy for all the data collected using various Kolari vision lenses. Moreover, an openly available balanced data set was used to compare the effect of the data set balance and unbalanced characteristics on the classification accuracy. The result showed that balanced data sets do not impact the outcome.
- Full Text:
- Date Issued: 2023
Multispectral Plant Disease Detection with Vision Transformer–Convolutional Neural Network Hybrid Approaches
- De Silva, Malitha, Brown, Dane L
- Authors: De Silva, Malitha , Brown, Dane L
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463428 , vital:76408 , 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.
- Full Text:
- Date Issued: 2023
- Authors: De Silva, Malitha , Brown, Dane L
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463428 , vital:76408 , 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.
- Full Text:
- Date Issued: 2023
Plant Disease Detection using Vision Transformers on Multispectral Natural Environment Images
- De Silva, Malitha, Brown, Dane L
- Authors: De Silva, Malitha , Brown, Dane L
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463456 , vital:76410 , xlink:href="https://ieeexplore.ieee.org/abstract/document/10220517"
- Description: Enhancing agricultural practices has become essential in mitigating global hunger. Over the years, significant technological advancements have been introduced to improve the quality and quantity of harvests by effectively managing weeds, pests, and diseases. Many studies have focused on identifying plant diseases, as this information aids in making informed decisions about applying fungicides and fertilizers. Advanced systems often employ a combination of image processing and deep learning techniques to identify diseases based on visible symptoms. However, these systems typically rely on pre-existing datasets or images captured in controlled environments. This study showcases the efficacy of utilizing multispectral images captured in visible and Near Infrared (NIR) ranges for identifying plant diseases in real-world environmental conditions. The collected datasets were classified using popular Vision Transformer (ViT) models, including ViT- S16, ViT-BI6, ViT-LI6 and ViT-B32. The results showed impressive training and test accuracies for all the data collected using diverse Kolari vision lenses with 93.71 % and 90.02 %, respectively. This work highlights the potential of utilizing advanced imaging techniques for accurate and reliable plant disease identification in practical field conditions.
- Full Text:
- Date Issued: 2023
- Authors: De Silva, Malitha , Brown, Dane L
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463456 , vital:76410 , xlink:href="https://ieeexplore.ieee.org/abstract/document/10220517"
- Description: Enhancing agricultural practices has become essential in mitigating global hunger. Over the years, significant technological advancements have been introduced to improve the quality and quantity of harvests by effectively managing weeds, pests, and diseases. Many studies have focused on identifying plant diseases, as this information aids in making informed decisions about applying fungicides and fertilizers. Advanced systems often employ a combination of image processing and deep learning techniques to identify diseases based on visible symptoms. However, these systems typically rely on pre-existing datasets or images captured in controlled environments. This study showcases the efficacy of utilizing multispectral images captured in visible and Near Infrared (NIR) ranges for identifying plant diseases in real-world environmental conditions. The collected datasets were classified using popular Vision Transformer (ViT) models, including ViT- S16, ViT-BI6, ViT-LI6 and ViT-B32. The results showed impressive training and test accuracies for all the data collected using diverse Kolari vision lenses with 93.71 % and 90.02 %, respectively. This work highlights the potential of utilizing advanced imaging techniques for accurate and reliable plant disease identification in practical field conditions.
- Full Text:
- Date Issued: 2023
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