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
Early dehydration detection using infrared imaging
- Poole, Louise C, Brown, Dane L, Connan, James
- Authors: Poole, Louise C , Brown, Dane L , Connan, James
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465656 , vital:76629 , xlink:href="https://www.researchgate.net/profile/Louise-Poole-3/publication/357578445_Early_Dehydration_Detection_Using_Infrared_Imaging/links/61d5664eb8305f7c4b231d50/Early-Dehydration-Detection-Using-Infrared-Imaging.pdf"
- Description: Crop loss and failure have devastating impacts on a country’s economy and food security. Developing effective and inexpensive systems to minimize crop loss has become essential. Recently, multispectral imaging—in particular visible and infrared imaging—have become popular for analyzing plants and show potential for early identification of plant stress. We created a directly comparable visible and infrared image dataset for dehydration in spinach leaves. We created and compared various models trained on both datasets and concluded that the models trained on the infrared dataset outperformed all of those trained on the visible dataset. In particular, the models trained to identify early signs of dehydration yielded 45% difference in accuracy, with the infrared model obtaining 70% accuracy and the visible model obtaining 25% accuracy. Infrared imaging thus shows promising potential for application in early plant stress and disease identification.
- Full Text:
- Date Issued: 2021
- Authors: Poole, Louise C , Brown, Dane L , Connan, James
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465656 , vital:76629 , xlink:href="https://www.researchgate.net/profile/Louise-Poole-3/publication/357578445_Early_Dehydration_Detection_Using_Infrared_Imaging/links/61d5664eb8305f7c4b231d50/Early-Dehydration-Detection-Using-Infrared-Imaging.pdf"
- Description: Crop loss and failure have devastating impacts on a country’s economy and food security. Developing effective and inexpensive systems to minimize crop loss has become essential. Recently, multispectral imaging—in particular visible and infrared imaging—have become popular for analyzing plants and show potential for early identification of plant stress. We created a directly comparable visible and infrared image dataset for dehydration in spinach leaves. We created and compared various models trained on both datasets and concluded that the models trained on the infrared dataset outperformed all of those trained on the visible dataset. In particular, the models trained to identify early signs of dehydration yielded 45% difference in accuracy, with the infrared model obtaining 70% accuracy and the visible model obtaining 25% accuracy. Infrared imaging thus shows promising potential for application in early plant stress and disease identification.
- Full Text:
- Date Issued: 2021
Investigating popular CNN architectures for plant disease detection
- Poole, Louise C, Brown, Dane L
- Authors: Poole, Louise C , Brown, Dane L
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465168 , vital:76579 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9519341"
- Description: Food production and food security have become increasingly important due to climate change and rising population numbers. One method to prevent crop loss is to develop a system to allow for early, efficient and accurate identification of plant diseases. CNNs often outperform previously popular machine learning algorithms. There are many existing CNN architectures. We compared and analysed the popular state-of-the-art architectures, namely ResNet, GoogLeNet and VGG, when trained for plant disease classification. We found that ResNet performed the best on the balanced Mendeley Leaves and PlantVillage datasets, obtaining 91.95% and 95.80% accuracy respectively. However, the ResNet architecture was relatively computationally expensive and slow to train. GoogLeNet obtained accuracies very close to those of ResNet with 89.35% and 94.59% achieved on the Mendeley Leaves and PlantVillage datasets respectively and could be considered a less computationally expensive alternative.
- Full Text:
- Date Issued: 2021
- Authors: Poole, Louise C , Brown, Dane L
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465168 , vital:76579 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9519341"
- Description: Food production and food security have become increasingly important due to climate change and rising population numbers. One method to prevent crop loss is to develop a system to allow for early, efficient and accurate identification of plant diseases. CNNs often outperform previously popular machine learning algorithms. There are many existing CNN architectures. We compared and analysed the popular state-of-the-art architectures, namely ResNet, GoogLeNet and VGG, when trained for plant disease classification. We found that ResNet performed the best on the balanced Mendeley Leaves and PlantVillage datasets, obtaining 91.95% and 95.80% accuracy respectively. However, the ResNet architecture was relatively computationally expensive and slow to train. GoogLeNet obtained accuracies very close to those of ResNet with 89.35% and 94.59% achieved on the Mendeley Leaves and PlantVillage datasets respectively and could be considered a less computationally expensive alternative.
- Full Text:
- Date Issued: 2021
Plant disease detection and classification for farmers and everyday gardeners
- Poole, Louise C, Brown, Dane L
- Authors: Poole, Louise C , Brown, Dane L
- Date: 2019
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465722 , vital:76635 , xlink:href="https://www.researchgate.net/profile/Dane-Brown-2/publication/335378684_Plant_Disease_Detection_and_Classification_for_Farmers_and_Everyday_Gardeners/links/5d611905299bf1f70b090b54/Plant-Disease-Detection-and-Classification-for-Farmers-and-Everyday-Gardeners.pdf"
- Description: Identifying and rating diseases by hand is an expensive, time consuming, subjective and unreliable method as compared to what computers can provide. Image processing and machine learning enable automated disease identification. Research has proven that automated disease identification systems can be used as a preventative measure against plant rot and death. This paper narrows down the best techniques to segment images of leaves toward improved classification of diseases found on those leaves. An investigation is conducted on image segmentation and machine learning techniques, including state-of-the-art systems, to determine the most appropriate approach to prevent death and rot in plants. Promising results were observed during testing, and show that a system can be implemented to assist with plant health that is relevant to both home gardeners and farmers.
- Full Text:
- Date Issued: 2019
- Authors: Poole, Louise C , Brown, Dane L
- Date: 2019
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465722 , vital:76635 , xlink:href="https://www.researchgate.net/profile/Dane-Brown-2/publication/335378684_Plant_Disease_Detection_and_Classification_for_Farmers_and_Everyday_Gardeners/links/5d611905299bf1f70b090b54/Plant-Disease-Detection-and-Classification-for-Farmers-and-Everyday-Gardeners.pdf"
- Description: Identifying and rating diseases by hand is an expensive, time consuming, subjective and unreliable method as compared to what computers can provide. Image processing and machine learning enable automated disease identification. Research has proven that automated disease identification systems can be used as a preventative measure against plant rot and death. This paper narrows down the best techniques to segment images of leaves toward improved classification of diseases found on those leaves. An investigation is conducted on image segmentation and machine learning techniques, including state-of-the-art systems, to determine the most appropriate approach to prevent death and rot in plants. Promising results were observed during testing, and show that a system can be implemented to assist with plant health that is relevant to both home gardeners and farmers.
- Full Text:
- Date Issued: 2019
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