Plant disease detection using deep learning on natural environment images
- De Silva, Malitha, Brown, Dane L
- Authors: De Silva, Malitha , Brown, Dane L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465212 , vital:76583 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9855925"
- Description: Improving agriculture is one of the major concerns today, as it helps reduce global hunger. In past years, many technological advancements have been introduced to enhance harvest quality and quantity by controlling and preventing weeds, pests, and diseases. Several studies have focused on identifying diseases in plants, as it helps to make decisions on spraying fungicides and fertilizers. State-of-the-art systems typically combine image processing and deep learning methods to identify conditions with visible symptoms. However, they use already available data sets or images taken in controlled environments. This study was conducted on two data sets of ten plants collected in a natural environment. The first dataset contained RGB Visible images, while the second contained Near-Infrared (NIR) images of healthy and diseased leaves. The visible image dataset showed higher training and validation accuracies than the NIR image dataset with ResNet, Inception, VGG and MobileNet architectures. For the visible image and NIR dataset, ResNet-50V2 outperformed other models with validation accuracies of 98.35% and 94.01%, respectively.
- Full Text:
- Date Issued: 2022
- Authors: De Silva, Malitha , Brown, Dane L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465212 , vital:76583 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9855925"
- Description: Improving agriculture is one of the major concerns today, as it helps reduce global hunger. In past years, many technological advancements have been introduced to enhance harvest quality and quantity by controlling and preventing weeds, pests, and diseases. Several studies have focused on identifying diseases in plants, as it helps to make decisions on spraying fungicides and fertilizers. State-of-the-art systems typically combine image processing and deep learning methods to identify conditions with visible symptoms. However, they use already available data sets or images taken in controlled environments. This study was conducted on two data sets of ten plants collected in a natural environment. The first dataset contained RGB Visible images, while the second contained Near-Infrared (NIR) images of healthy and diseased leaves. The visible image dataset showed higher training and validation accuracies than the NIR image dataset with ResNet, Inception, VGG and MobileNet architectures. For the visible image and NIR dataset, ResNet-50V2 outperformed other models with validation accuracies of 98.35% and 94.01%, respectively.
- Full Text:
- Date Issued: 2022
Plant disease detection using multispectral imaging
- De Silva, Malitha, Brown, Dane L
- Authors: De Silva, Malitha , Brown, Dane L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463439 , vital:76409 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-031-35641-4_24"
- Description: People worldwide are undergoing many challenges, including food scarcity. Many pieces of research are now focused on improving agriculture to increase the harvest and reduce the cost. Identifying plant diseases and pests in the early stages helps to enhance the yield and reduce costs. However, most plant disease identification research with computer vision has been done with images taken in controlled environments on publically available data sets. Near-Infrared (NIR) imaging is a favourable approach for identifying plant diseases. Therefore, this study collected NIR images of healthy and diseased leaves in the natural environment. The dataset is tested with eight Convolutional Neural Network (CNN) models with different train-test splits ranging from 10:90 to 90:10. The evaluated models attained their highest training and test accuracies from the 70:30 split onwards. Xception outperformed all the other models in all train-test splits and achieved 100% accuracy, precision and recall in the 80:20 train-test split.
- Full Text:
- Date Issued: 2022
- Authors: De Silva, Malitha , Brown, Dane L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463439 , vital:76409 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-031-35641-4_24"
- Description: People worldwide are undergoing many challenges, including food scarcity. Many pieces of research are now focused on improving agriculture to increase the harvest and reduce the cost. Identifying plant diseases and pests in the early stages helps to enhance the yield and reduce costs. However, most plant disease identification research with computer vision has been done with images taken in controlled environments on publically available data sets. Near-Infrared (NIR) imaging is a favourable approach for identifying plant diseases. Therefore, this study collected NIR images of healthy and diseased leaves in the natural environment. The dataset is tested with eight Convolutional Neural Network (CNN) models with different train-test splits ranging from 10:90 to 90:10. The evaluated models attained their highest training and test accuracies from the 70:30 split onwards. Xception outperformed all the other models in all train-test splits and achieved 100% accuracy, precision and recall in the 80:20 train-test split.
- Full Text:
- Date Issued: 2022
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