Evaluation of deep learning models for RGB image-based detection of potato virus y strain symptoms (O, NO, and NTN) in potato plants
Potato virus Y (PVY) has been a long-standing problem for potato growers over the world, due to its ability to cause significant reductions in crop yields. The yield losses due to PVY may range from 10% to 80%, depending on the severity of the infection and the potato variety. The new necrotic strai...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375524003599 |
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| author | Charanpreet Singh Gurjit S. Randhawa Aitazaz A. Farooque Yuvraj S. Gill Lokesh Kumar KM Mathuresh Singh Khalil Al-Mughrabi |
| author_facet | Charanpreet Singh Gurjit S. Randhawa Aitazaz A. Farooque Yuvraj S. Gill Lokesh Kumar KM Mathuresh Singh Khalil Al-Mughrabi |
| author_sort | Charanpreet Singh |
| collection | DOAJ |
| description | Potato virus Y (PVY) has been a long-standing problem for potato growers over the world, due to its ability to cause significant reductions in crop yields. The yield losses due to PVY may range from 10% to 80%, depending on the severity of the infection and the potato variety. The new necrotic strains of PVY cause mild symptoms in the foliage, making it challenging to detect infected plants. Consequently, identifying and disposing of infected plants (known as “roguing”) has become more difficult. There is a growing demand to create solutions that aid growers in identifying potato plants that have been infected with PVY. In past studies, deep learning-based convolutional neural networks (CNNs) have shown the ability to successfully make distinctions between various healthy plants, disease plants, and weeds. In this study, the use of these models for the detection of infected plants with different strains of PVY has been explored and extended. Different deep learning models, specifically EfficientNet, VGGNet-19, DenseNet-201 and ResNet-101 are trained on the imagery dataset of healthy and PVY-infected potato plants grown under greenhouse conditions. The evaluation metrics used were accuracy, precision, recall, and F1 Score. The trained models achieved classification accuracy scores of 85% while classifying the healthy and PVY-infected potato plants. The models were also able to accurately detect PVY-infected plants even when the symptoms were mild, which is essential for early detection and prevention of the spread of the virus. These models may assist roguers in the real-time identification of PVY-infected plants that may help in controlling the disease spread and improving the crop yield. |
| format | Article |
| id | doaj-art-e72c7e6837cf48a8840e23171e3833ee |
| institution | DOAJ |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-e72c7e6837cf48a8840e23171e3833ee2025-08-20T02:52:20ZengElsevierSmart Agricultural Technology2772-37552025-03-011010075510.1016/j.atech.2024.100755Evaluation of deep learning models for RGB image-based detection of potato virus y strain symptoms (O, NO, and NTN) in potato plantsCharanpreet Singh0Gurjit S. Randhawa1Aitazaz A. Farooque2Yuvraj S. Gill3Lokesh Kumar KM4Mathuresh Singh5Khalil Al-Mughrabi6University of Prince Edward Island, Faculty of Sustainable Design & Engineering, Charlottetown, C1A 4P3, PE, CanadaUniversity of Guelph, School of Computer Science, Guelph, N1G 2W1, ON, Canada; Corresponding author.University of Prince Edward Island, Faculty of Sustainable Design & Engineering, Charlottetown, C1A 4P3, PE, Canada; University of Prince Edward Island, Canadian Centre for Climate Change and Adaptation, Saint Peters Bay, C0A 2A0, PE, CanadaUniversity of Prince Edward Island, Faculty of Sustainable Design & Engineering, Charlottetown, C1A 4P3, PE, CanadaDepartment of Computer and Communication Engineering, Amrita Vishwa Vidyapeetham, Chennai, Tamil Nadu, IndiaAgricultural Certification Services Inc., 1030 Lincoln Road, Fredericton, E3B 8B7, NB, CanadaNew Brunswick Department of Agriculture, Aquaculture and Fisheries, 39 Barker Lane, Wicklow, E7L 3S4, NB, CanadaPotato virus Y (PVY) has been a long-standing problem for potato growers over the world, due to its ability to cause significant reductions in crop yields. The yield losses due to PVY may range from 10% to 80%, depending on the severity of the infection and the potato variety. The new necrotic strains of PVY cause mild symptoms in the foliage, making it challenging to detect infected plants. Consequently, identifying and disposing of infected plants (known as “roguing”) has become more difficult. There is a growing demand to create solutions that aid growers in identifying potato plants that have been infected with PVY. In past studies, deep learning-based convolutional neural networks (CNNs) have shown the ability to successfully make distinctions between various healthy plants, disease plants, and weeds. In this study, the use of these models for the detection of infected plants with different strains of PVY has been explored and extended. Different deep learning models, specifically EfficientNet, VGGNet-19, DenseNet-201 and ResNet-101 are trained on the imagery dataset of healthy and PVY-infected potato plants grown under greenhouse conditions. The evaluation metrics used were accuracy, precision, recall, and F1 Score. The trained models achieved classification accuracy scores of 85% while classifying the healthy and PVY-infected potato plants. The models were also able to accurately detect PVY-infected plants even when the symptoms were mild, which is essential for early detection and prevention of the spread of the virus. These models may assist roguers in the real-time identification of PVY-infected plants that may help in controlling the disease spread and improving the crop yield.http://www.sciencedirect.com/science/article/pii/S2772375524003599PVYSmart agricultureMachine learningArtificial intelligenceCNN |
| spellingShingle | Charanpreet Singh Gurjit S. Randhawa Aitazaz A. Farooque Yuvraj S. Gill Lokesh Kumar KM Mathuresh Singh Khalil Al-Mughrabi Evaluation of deep learning models for RGB image-based detection of potato virus y strain symptoms (O, NO, and NTN) in potato plants Smart Agricultural Technology PVY Smart agriculture Machine learning Artificial intelligence CNN |
| title | Evaluation of deep learning models for RGB image-based detection of potato virus y strain symptoms (O, NO, and NTN) in potato plants |
| title_full | Evaluation of deep learning models for RGB image-based detection of potato virus y strain symptoms (O, NO, and NTN) in potato plants |
| title_fullStr | Evaluation of deep learning models for RGB image-based detection of potato virus y strain symptoms (O, NO, and NTN) in potato plants |
| title_full_unstemmed | Evaluation of deep learning models for RGB image-based detection of potato virus y strain symptoms (O, NO, and NTN) in potato plants |
| title_short | Evaluation of deep learning models for RGB image-based detection of potato virus y strain symptoms (O, NO, and NTN) in potato plants |
| title_sort | evaluation of deep learning models for rgb image based detection of potato virus y strain symptoms o no and ntn in potato plants |
| topic | PVY Smart agriculture Machine learning Artificial intelligence CNN |
| url | http://www.sciencedirect.com/science/article/pii/S2772375524003599 |
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