Potato Late Blight Disease Detection on UAV Multispectral Imagery.
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| Title: | Potato Late Blight Disease Detection on UAV Multispectral Imagery. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18091292 |
