3D-CNN detection of systemic symptoms induced by different Potexvirus infections in four Nicotiana benthamiana genotypes using leaf hyperspectral imaging
Abstract Purpose Hyperspectral imaging combined with machine learning offers a promising, cost-effective alternative to invasive chemical analysis for early plant disease detection. In this study, the use of 3D Convolutional Neural Networks (3D-CNNs) was explored to detect presymptomatic viral infec...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-02-01
|
| Series: | Plant Methods |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13007-025-01337-0 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850197424616767488 |
|---|---|
| author | Rizos-Theodoros Chadoulis Ioannis Livieratos Ioannis Manakos Theodore Spanos Zeinab Marouni Christos Kalogeropoulos Constantine Kotropoulos |
| author_facet | Rizos-Theodoros Chadoulis Ioannis Livieratos Ioannis Manakos Theodore Spanos Zeinab Marouni Christos Kalogeropoulos Constantine Kotropoulos |
| author_sort | Rizos-Theodoros Chadoulis |
| collection | DOAJ |
| description | Abstract Purpose Hyperspectral imaging combined with machine learning offers a promising, cost-effective alternative to invasive chemical analysis for early plant disease detection. In this study, the use of 3D Convolutional Neural Networks (3D-CNNs) was explored to detect presymptomatic viral infections in the model plant Nicotiana benthamiana L. and assess the generalization of these models across different plant genotypes. Methods Four genotypes of Nicotiana benthamiana L. (wild-type, DCL2/4, AGO2, and NahG) were inoculated with different potexviruses (PepMV mild or severe strain, PVX, BaMV). Viral infection was verified via northern blot analysis at 5 and 10 days post inoculation (DPI). Hyperspectral images were captured over 10 days following inoculation, focusing on the top 3 leaves where symptoms typically appear. The dataset was carefully processed to remove errors, and raster masks were generated to isolate only the leaf pixels. The Extremely Randomized Trees algorithm was used for Effective Wavelength selection, and a novel 3D-CNN architecture was developed to classify $$16 \times 16 \times 16$$ 16 × 16 × 16 nonoverlapping cubes extracted from the unmasked leaf surfaces. The aim was to classify each cube into healthy or diseased for each of the four viruses at different time points. Results Accuracies of $$0.78$$ 0.78 – $$0.87$$ 0.87 were achieved for AGO2 mutants at the cube level, and overall plant-level accuracies of $$0.68$$ 0.68 – $$0.89$$ 0.89 . The model’s generalization capabilities were tested across other genotypes, yielding accuracies of up to $$0.75$$ 0.75 for DCL2/4, $$0.83$$ 0.83 for NahG, and $$0.78$$ 0.78 for the wild-type. The timing of disease detection was also assessed, finding that accuracies approached 0.8 as early as $$6$$ 6 – $$8$$ 8 DPI depending on the virus. The results were validated against northern blot analyses and benchmarked against another state-of-the-art methodology for Nicotiana benthamiana viral infections, achieving superior overall classification accuracies. Conclusion The proposed patch-based method demonstrated key advantages: (a) exploiting both spectral and textural information, (b) deriving a large training dataset from few hyperspectral images, (c) providing localized classification explainability within leaf regions, and (d) achieving high accuracy for early detection of viral infections. |
| format | Article |
| id | doaj-art-45bbc388d6404cca971b187604e4a94f |
| institution | OA Journals |
| issn | 1746-4811 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Plant Methods |
| spelling | doaj-art-45bbc388d6404cca971b187604e4a94f2025-08-20T02:13:08ZengBMCPlant Methods1746-48112025-02-0121112310.1186/s13007-025-01337-03D-CNN detection of systemic symptoms induced by different Potexvirus infections in four Nicotiana benthamiana genotypes using leaf hyperspectral imagingRizos-Theodoros Chadoulis0Ioannis Livieratos1Ioannis Manakos2Theodore Spanos3Zeinab Marouni4Christos Kalogeropoulos5Constantine Kotropoulos6Department of Informatics, Aristotle University of ThessalonikiDepartment of Sustainable Agriculture, Mediterranean Agronomic Institute of ChaniaInformation Technologies Institute, Centre for Research and Technology HellasDepartment of Sustainable Agriculture, Mediterranean Agronomic Institute of ChaniaDepartment of Sustainable Agriculture, Mediterranean Agronomic Institute of ChaniaInformation Technologies Institute, Centre for Research and Technology HellasDepartment of Informatics, Aristotle University of ThessalonikiAbstract Purpose Hyperspectral imaging combined with machine learning offers a promising, cost-effective alternative to invasive chemical analysis for early plant disease detection. In this study, the use of 3D Convolutional Neural Networks (3D-CNNs) was explored to detect presymptomatic viral infections in the model plant Nicotiana benthamiana L. and assess the generalization of these models across different plant genotypes. Methods Four genotypes of Nicotiana benthamiana L. (wild-type, DCL2/4, AGO2, and NahG) were inoculated with different potexviruses (PepMV mild or severe strain, PVX, BaMV). Viral infection was verified via northern blot analysis at 5 and 10 days post inoculation (DPI). Hyperspectral images were captured over 10 days following inoculation, focusing on the top 3 leaves where symptoms typically appear. The dataset was carefully processed to remove errors, and raster masks were generated to isolate only the leaf pixels. The Extremely Randomized Trees algorithm was used for Effective Wavelength selection, and a novel 3D-CNN architecture was developed to classify $$16 \times 16 \times 16$$ 16 × 16 × 16 nonoverlapping cubes extracted from the unmasked leaf surfaces. The aim was to classify each cube into healthy or diseased for each of the four viruses at different time points. Results Accuracies of $$0.78$$ 0.78 – $$0.87$$ 0.87 were achieved for AGO2 mutants at the cube level, and overall plant-level accuracies of $$0.68$$ 0.68 – $$0.89$$ 0.89 . The model’s generalization capabilities were tested across other genotypes, yielding accuracies of up to $$0.75$$ 0.75 for DCL2/4, $$0.83$$ 0.83 for NahG, and $$0.78$$ 0.78 for the wild-type. The timing of disease detection was also assessed, finding that accuracies approached 0.8 as early as $$6$$ 6 – $$8$$ 8 DPI depending on the virus. The results were validated against northern blot analyses and benchmarked against another state-of-the-art methodology for Nicotiana benthamiana viral infections, achieving superior overall classification accuracies. Conclusion The proposed patch-based method demonstrated key advantages: (a) exploiting both spectral and textural information, (b) deriving a large training dataset from few hyperspectral images, (c) providing localized classification explainability within leaf regions, and (d) achieving high accuracy for early detection of viral infections.https://doi.org/10.1186/s13007-025-01337-0Artificial intelligenceConvolutional neural networksDimensionality reductionHyperspectral imagingEarly plant disease detection |
| spellingShingle | Rizos-Theodoros Chadoulis Ioannis Livieratos Ioannis Manakos Theodore Spanos Zeinab Marouni Christos Kalogeropoulos Constantine Kotropoulos 3D-CNN detection of systemic symptoms induced by different Potexvirus infections in four Nicotiana benthamiana genotypes using leaf hyperspectral imaging Plant Methods Artificial intelligence Convolutional neural networks Dimensionality reduction Hyperspectral imaging Early plant disease detection |
| title | 3D-CNN detection of systemic symptoms induced by different Potexvirus infections in four Nicotiana benthamiana genotypes using leaf hyperspectral imaging |
| title_full | 3D-CNN detection of systemic symptoms induced by different Potexvirus infections in four Nicotiana benthamiana genotypes using leaf hyperspectral imaging |
| title_fullStr | 3D-CNN detection of systemic symptoms induced by different Potexvirus infections in four Nicotiana benthamiana genotypes using leaf hyperspectral imaging |
| title_full_unstemmed | 3D-CNN detection of systemic symptoms induced by different Potexvirus infections in four Nicotiana benthamiana genotypes using leaf hyperspectral imaging |
| title_short | 3D-CNN detection of systemic symptoms induced by different Potexvirus infections in four Nicotiana benthamiana genotypes using leaf hyperspectral imaging |
| title_sort | 3d cnn detection of systemic symptoms induced by different potexvirus infections in four nicotiana benthamiana genotypes using leaf hyperspectral imaging |
| topic | Artificial intelligence Convolutional neural networks Dimensionality reduction Hyperspectral imaging Early plant disease detection |
| url | https://doi.org/10.1186/s13007-025-01337-0 |
| work_keys_str_mv | AT rizostheodoroschadoulis 3dcnndetectionofsystemicsymptomsinducedbydifferentpotexvirusinfectionsinfournicotianabenthamianagenotypesusingleafhyperspectralimaging AT ioannislivieratos 3dcnndetectionofsystemicsymptomsinducedbydifferentpotexvirusinfectionsinfournicotianabenthamianagenotypesusingleafhyperspectralimaging AT ioannismanakos 3dcnndetectionofsystemicsymptomsinducedbydifferentpotexvirusinfectionsinfournicotianabenthamianagenotypesusingleafhyperspectralimaging AT theodorespanos 3dcnndetectionofsystemicsymptomsinducedbydifferentpotexvirusinfectionsinfournicotianabenthamianagenotypesusingleafhyperspectralimaging AT zeinabmarouni 3dcnndetectionofsystemicsymptomsinducedbydifferentpotexvirusinfectionsinfournicotianabenthamianagenotypesusingleafhyperspectralimaging AT christoskalogeropoulos 3dcnndetectionofsystemicsymptomsinducedbydifferentpotexvirusinfectionsinfournicotianabenthamianagenotypesusingleafhyperspectralimaging AT constantinekotropoulos 3dcnndetectionofsystemicsymptomsinducedbydifferentpotexvirusinfectionsinfournicotianabenthamianagenotypesusingleafhyperspectralimaging |