Potato Late Blight Disease Detection on UAV Multispectral Imagery.
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| Title: | Potato Late Blight Disease Detection on UAV Multispectral Imagery. |
|---|---|
| Authors: | Kaviani, Mohadeseh1 (AUTHOR), Leblon, Brigitte1,2 (AUTHOR) bleblon@lakeheadu.ca, Akilan, Thangarajah2,3 (AUTHOR), Amishev, Dzhamal1,4 (AUTHOR), LaRocque, Armand3 (AUTHOR), Haddadi, Ata4 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1292. 20p. |
| Subjects: | Late blight of potato, Multispectral imaging, Plant indicators, Machine learning, Object recognition (Computer vision), Deep learning |
| Abstract: | Highlights: What are the main findings? A Mask R-CNN model with a ResNeXt-101 backbone achieved the best performance (F1-score = 84.2%) in potato plant detection when transfer learning was applied using a Mask R-CNN model pretrained on apple orchard data. A DINOv3-based Mask R-CNN achieved the best performance (F1-score = 69.0%) in plant health classification despite the limited number of labelled samples, which reflects realistic field conditions in UAV-based potato late blight disease detection studies. A decision tree machine learning (ML) algorithm applied to vegetation indices achieved the best performance (F1-score = 66.7%). Red-edge and chlorophyll-related vegetation indices were the most discriminative features. What are the implications of the main findings? UAV multispectral imagery enables effective detection of potato late blight at the plant level. Red-edge-based vegetation indices play a key role in capturing disease-induced physiological changes. Both deep learning (DL) and combined DL-ML approaches provide scalable solutions for potato late blight disease monitoring. In this study, Mask R-CNN was applied to 5-band raw reflectance images to detect potato plants in UAV images. The highest model performance across all metrics was achieved with a ResNeXt-101 backbone and transfer learning from the same model trained on apple orchard data. An F-1 score of 84.2% was achieved. To determine whether the plant was infected with PLB, two methods were used. In the first method, a Mask R-CNN with a DINOv3 small variant backbone was applied to 5-band raw reflectance images. The highest achieved F1-score was 69.05%. In the second method, classical ML classifiers were applied to the 5-band raw reflectance images and 16 associated vegetation index images. The highest F1-score (66.71%) was obtained with a decision tree classifier applied to the 16 vegetation index images. Feature importance analysis indicated that chlorophyll- and red-edge-related indices, such as CIgreen, TCARI, OSAVI2, and Red-edge NDVI, were the most discriminative features for distinguishing healthy and unhealthy potato plants. These results show the effectiveness of combining deep learning and machine learning approaches for potato late blight detection using UAV multispectral imagery. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 193715323 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Potato Late Blight Disease Detection on UAV Multispectral Imagery. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kaviani%2C+Mohadeseh%22">Kaviani, Mohadeseh</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Leblon%2C+Brigitte%22">Leblon, Brigitte</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> bleblon@lakeheadu.ca</i><br /><searchLink fieldCode="AR" term="%22Akilan%2C+Thangarajah%22">Akilan, Thangarajah</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Amishev%2C+Dzhamal%22">Amishev, Dzhamal</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22LaRocque%2C+Armand%22">LaRocque, Armand</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Haddadi%2C+Ata%22">Haddadi, Ata</searchLink><relatesTo>4</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. May2026, Vol. 18 Issue 9, p1292. 20p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Late+blight+of+potato%22">Late blight of potato</searchLink><br /><searchLink fieldCode="DE" term="%22Multispectral+imaging%22">Multispectral imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Plant+indicators%22">Plant indicators</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? A Mask R-CNN model with a ResNeXt-101 backbone achieved the best performance (F1-score = 84.2%) in potato plant detection when transfer learning was applied using a Mask R-CNN model pretrained on apple orchard data. A DINOv3-based Mask R-CNN achieved the best performance (F1-score = 69.0%) in plant health classification despite the limited number of labelled samples, which reflects realistic field conditions in UAV-based potato late blight disease detection studies. A decision tree machine learning (ML) algorithm applied to vegetation indices achieved the best performance (F1-score = 66.7%). Red-edge and chlorophyll-related vegetation indices were the most discriminative features. What are the implications of the main findings? UAV multispectral imagery enables effective detection of potato late blight at the plant level. Red-edge-based vegetation indices play a key role in capturing disease-induced physiological changes. Both deep learning (DL) and combined DL-ML approaches provide scalable solutions for potato late blight disease monitoring. In this study, Mask R-CNN was applied to 5-band raw reflectance images to detect potato plants in UAV images. The highest model performance across all metrics was achieved with a ResNeXt-101 backbone and transfer learning from the same model trained on apple orchard data. An F-1 score of 84.2% was achieved. To determine whether the plant was infected with PLB, two methods were used. In the first method, a Mask R-CNN with a DINOv3 small variant backbone was applied to 5-band raw reflectance images. The highest achieved F1-score was 69.05%. In the second method, classical ML classifiers were applied to the 5-band raw reflectance images and 16 associated vegetation index images. The highest F1-score (66.71%) was obtained with a decision tree classifier applied to the 16 vegetation index images. Feature importance analysis indicated that chlorophyll- and red-edge-related indices, such as CIgreen, TCARI, OSAVI2, and Red-edge NDVI, were the most discriminative features for distinguishing healthy and unhealthy potato plants. These results show the effectiveness of combining deep learning and machine learning approaches for potato late blight detection using UAV multispectral imagery. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18091292 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 1292 Subjects: – SubjectFull: Late blight of potato Type: general – SubjectFull: Multispectral imaging Type: general – SubjectFull: Plant indicators Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Object recognition (Computer vision) Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: Potato Late Blight Disease Detection on UAV Multispectral Imagery. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kaviani, Mohadeseh – PersonEntity: Name: NameFull: Leblon, Brigitte – PersonEntity: Name: NameFull: Akilan, Thangarajah – PersonEntity: Name: NameFull: Amishev, Dzhamal – PersonEntity: Name: NameFull: LaRocque, Armand – PersonEntity: Name: NameFull: Haddadi, Ata IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 9 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
