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
Efficient Plant Disease Detection and Classification for Android
- Brown, Dane L, Mazibuko, Sifisokuhle
- Authors: Brown, Dane L , Mazibuko, Sifisokuhle
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464096 , vital:76475 , xlink:href="https://link.springer.com/chapter/10.1007/978-981-99-1624-5_39"
- Description: This paper investigates the feasibility of using a CNN model to diagnose plant diseases in the wild. Plant diseases are a major risk to ecosystems, human and animal health, and the quality of life overall. They may reduce farm productivity drastically, leaving farmers with financial losses and food insecurity. Small-scale farmers and producers cannot pay for an expert to look at their plants for plant diseases because it would cost too much. A mobile solution is thus built for the Android platform that utilises a unified deep learning model to diagnose plant diseases and provide farmers with treatment information. The literature-recommended CNN architectures were first analysed on the PlantVillage dataset, and the best-performing model was trained for integration into the application. While training on the tomato subset of the PlantVillage dataset, the VGG16 and InceptionV3 networks achieved a higher F1-score of 94.49% than the MobileNetsV3Large and EfficientNetB0 networks (without parameter tuning). The VGG model achieved 94.43% accuracy and 0.24 loss on the RGB PlantVillage dataset, outperforming the segmented and greyscaled datasets, and was therefore chosen for use in the application. When tested on complex data collected in the wild, the VGG16 model trained on the RGB dataset yielded an accuracy of 63.02%. Thus, this research revealed the discrepancy between simple and real-world data, as well as the viability of present methodologies for future research.
- Full Text:
- Date Issued: 2023
- Authors: Brown, Dane L , Mazibuko, Sifisokuhle
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464096 , vital:76475 , xlink:href="https://link.springer.com/chapter/10.1007/978-981-99-1624-5_39"
- Description: This paper investigates the feasibility of using a CNN model to diagnose plant diseases in the wild. Plant diseases are a major risk to ecosystems, human and animal health, and the quality of life overall. They may reduce farm productivity drastically, leaving farmers with financial losses and food insecurity. Small-scale farmers and producers cannot pay for an expert to look at their plants for plant diseases because it would cost too much. A mobile solution is thus built for the Android platform that utilises a unified deep learning model to diagnose plant diseases and provide farmers with treatment information. The literature-recommended CNN architectures were first analysed on the PlantVillage dataset, and the best-performing model was trained for integration into the application. While training on the tomato subset of the PlantVillage dataset, the VGG16 and InceptionV3 networks achieved a higher F1-score of 94.49% than the MobileNetsV3Large and EfficientNetB0 networks (without parameter tuning). The VGG model achieved 94.43% accuracy and 0.24 loss on the RGB PlantVillage dataset, outperforming the segmented and greyscaled datasets, and was therefore chosen for use in the application. When tested on complex data collected in the wild, the VGG16 model trained on the RGB dataset yielded an accuracy of 63.02%. Thus, this research revealed the discrepancy between simple and real-world data, as well as the viability of present methodologies for future research.
- Full Text:
- Date Issued: 2023
Enhanced plant species and early water stress detection using visible and near-infrared spectra
- Brown, Dane L, Poole, Louise C
- Authors: Brown, Dane L , Poole, Louise C
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463384 , vital:76404 , xlink:href="https://link.springer.com/chapter/10.1007/978-981-19-9819-5_55"
- Description: This paper reports on recent successful work aimed at preventing crop loss and failure before visible symptoms are present. Food security is critical, especially after the COVID-19 pandemic. Detecting early-stage plant stresses in agriculture is essential in minimizing crop damage and maximizing yield. Identification of both the stress type and cause is a non-trivial multitask classification problem. However, the application of spectroscopy to early plant diseases and stress detection has become viable with recent advancements in technology. Suitable frequencies of the electromagnetic spectrum and machine learning algorithms were thus first investigated. This guided data collection in two sessions by capturing standard visible images in contrast with images from multiple spectra (VIS-IR). These images consisted of six plant species that were carefully monitored from healthy to dehydrated stages. Promising results were achieved using VIS-IR compared to standard visible images on three deep learning architectures. Statistically, significant accuracy improvements were shown for VIS-IR for early dehydration detection, where ResNet-44 modelling of VIS-IR input yielded 92.5% accuracy compared to 77.5% on visible input on general plant species. Moreover, ResNet-44 achieved good species separation.
- Full Text:
- Date Issued: 2023
- Authors: Brown, Dane L , Poole, Louise C
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463384 , vital:76404 , xlink:href="https://link.springer.com/chapter/10.1007/978-981-19-9819-5_55"
- Description: This paper reports on recent successful work aimed at preventing crop loss and failure before visible symptoms are present. Food security is critical, especially after the COVID-19 pandemic. Detecting early-stage plant stresses in agriculture is essential in minimizing crop damage and maximizing yield. Identification of both the stress type and cause is a non-trivial multitask classification problem. However, the application of spectroscopy to early plant diseases and stress detection has become viable with recent advancements in technology. Suitable frequencies of the electromagnetic spectrum and machine learning algorithms were thus first investigated. This guided data collection in two sessions by capturing standard visible images in contrast with images from multiple spectra (VIS-IR). These images consisted of six plant species that were carefully monitored from healthy to dehydrated stages. Promising results were achieved using VIS-IR compared to standard visible images on three deep learning architectures. Statistically, significant accuracy improvements were shown for VIS-IR for early dehydration detection, where ResNet-44 modelling of VIS-IR input yielded 92.5% accuracy compared to 77.5% on visible input on general plant species. Moreover, ResNet-44 achieved good species separation.
- Full Text:
- Date Issued: 2023
Using Technology to Teach a New Generation
- Connan, James, Brown, Dane L, Watkins, Caroline
- Authors: Connan, James , Brown, Dane L , Watkins, Caroline
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465223 , vital:76584 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-030-92858-2_8"
- Description: Introductory programming courses attract students from diverse backgrounds in terms of ability, motivation and experience. This paper introduces two technological tools, Thonny and Runestone Academy, that can be used to enhance introductory courses. These tools enable instructors to track the progress of individual students. This allows for the early identification of students that are not keeping up with the course and allows for early intervention in such cases. Overall this leads to a better course with higher throughput and better student retention.
- Full Text:
- Date Issued: 2021
- Authors: Connan, James , Brown, Dane L , Watkins, Caroline
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465223 , vital:76584 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-030-92858-2_8"
- Description: Introductory programming courses attract students from diverse backgrounds in terms of ability, motivation and experience. This paper introduces two technological tools, Thonny and Runestone Academy, that can be used to enhance introductory courses. These tools enable instructors to track the progress of individual students. This allows for the early identification of students that are not keeping up with the course and allows for early intervention in such cases. Overall this leads to a better course with higher throughput and better student retention.
- Full Text:
- Date Issued: 2021
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