Quality assurance of hyperspectral imaging systems for neural network supported plant phenotyping
Abstract Background This research proposes an easy to apply quality assurance pipeline for hyperspectral imaging (HSI) systems used for plant phenotyping. Furthermore, a concept for the analysis of quality assured hyperspectral images to investigate plant disease progress is proposed. The quality as...
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| Format: | Article |
| Language: | English |
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BMC
2024-12-01
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| Series: | Plant Methods |
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| Online Access: | https://doi.org/10.1186/s13007-024-01315-y |
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| author | Justus Detring Abel Barreto Anne-Katrin Mahlein Stefan Paulus |
| author_facet | Justus Detring Abel Barreto Anne-Katrin Mahlein Stefan Paulus |
| author_sort | Justus Detring |
| collection | DOAJ |
| description | Abstract Background This research proposes an easy to apply quality assurance pipeline for hyperspectral imaging (HSI) systems used for plant phenotyping. Furthermore, a concept for the analysis of quality assured hyperspectral images to investigate plant disease progress is proposed. The quality assurance was applied to a handheld line scanning HSI-system consisting of evaluating spatial and spectral quality parameters as well as the integrated illumination. To test the spatial accuracy at different working distances, the sine-wave-based spatial frequency response (s-SFR) was analysed. The spectral accuracy was assessed by calculating the correlation of calibration-material measurements between the HSI-system and a non-imaging spectrometer. Additionally, different illumination systems were evaluated by analysing the spectral response of sugar beet canopies. As a use case, time series HSI measurements of sugar beet plants infested with Cercospora leaf spot (CLS) were performed to estimate the disease severity using convolutional neural network (CNN) supported data analysis. Results The measurements of the calibration material were highly correlated with those of the non-imaging spectrometer (r>0.99). The resolution limit was narrowly missed at each of the tested working distances. Slight sharpness differences within individual images could be detected. The use of the integrated LED illumination for HSI can cause a distortion of the spectral response at 677nm and 752nm. The performance for CLS diseased pixel detection of the established CNN was sufficient to estimate a reliable disease severity progression from quality assured hyperspectral measurements with external illumination. Conclusion The quality assurance pipeline was successfully applied to evaluate a handheld HSI-system. The s-SFR analysis is a valuable method for assessing the spatial accuracy of HSI-systems. Comparing measurements between HSI-systems and a non-imaging spectrometer can provide reliable results on the spectral accuracy of the tested system. This research emphasizes the importance of evenly distributed diffuse illumination for HSI. Although the tested system showed shortcomings in image resolution, sharpness, and illumination, the high spectral accuracy of the tested HSI-system, supported by external illumination, enabled the establishment of a neural network-based concept to determine the severity and progression of CLS. The data driven quality assurance pipeline can be easily applied to any other HSI-system to ensure high quality HSI. |
| format | Article |
| id | doaj-art-629ab8ed510d4b119dbcd92f68afca36 |
| institution | OA Journals |
| issn | 1746-4811 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Plant Methods |
| spelling | doaj-art-629ab8ed510d4b119dbcd92f68afca362025-08-20T01:57:15ZengBMCPlant Methods1746-48112024-12-0120111610.1186/s13007-024-01315-yQuality assurance of hyperspectral imaging systems for neural network supported plant phenotypingJustus Detring0Abel Barreto1Anne-Katrin Mahlein2Stefan Paulus3Institute of Sugar Beet ResearchInstitute of Sugar Beet ResearchInstitute of Sugar Beet ResearchInstitute of Sugar Beet ResearchAbstract Background This research proposes an easy to apply quality assurance pipeline for hyperspectral imaging (HSI) systems used for plant phenotyping. Furthermore, a concept for the analysis of quality assured hyperspectral images to investigate plant disease progress is proposed. The quality assurance was applied to a handheld line scanning HSI-system consisting of evaluating spatial and spectral quality parameters as well as the integrated illumination. To test the spatial accuracy at different working distances, the sine-wave-based spatial frequency response (s-SFR) was analysed. The spectral accuracy was assessed by calculating the correlation of calibration-material measurements between the HSI-system and a non-imaging spectrometer. Additionally, different illumination systems were evaluated by analysing the spectral response of sugar beet canopies. As a use case, time series HSI measurements of sugar beet plants infested with Cercospora leaf spot (CLS) were performed to estimate the disease severity using convolutional neural network (CNN) supported data analysis. Results The measurements of the calibration material were highly correlated with those of the non-imaging spectrometer (r>0.99). The resolution limit was narrowly missed at each of the tested working distances. Slight sharpness differences within individual images could be detected. The use of the integrated LED illumination for HSI can cause a distortion of the spectral response at 677nm and 752nm. The performance for CLS diseased pixel detection of the established CNN was sufficient to estimate a reliable disease severity progression from quality assured hyperspectral measurements with external illumination. Conclusion The quality assurance pipeline was successfully applied to evaluate a handheld HSI-system. The s-SFR analysis is a valuable method for assessing the spatial accuracy of HSI-systems. Comparing measurements between HSI-systems and a non-imaging spectrometer can provide reliable results on the spectral accuracy of the tested system. This research emphasizes the importance of evenly distributed diffuse illumination for HSI. Although the tested system showed shortcomings in image resolution, sharpness, and illumination, the high spectral accuracy of the tested HSI-system, supported by external illumination, enabled the establishment of a neural network-based concept to determine the severity and progression of CLS. The data driven quality assurance pipeline can be easily applied to any other HSI-system to ensure high quality HSI.https://doi.org/10.1186/s13007-024-01315-yImage resolutionImage sharpnessSpectral accuracySpatial accuracyIlluminationMachine learning |
| spellingShingle | Justus Detring Abel Barreto Anne-Katrin Mahlein Stefan Paulus Quality assurance of hyperspectral imaging systems for neural network supported plant phenotyping Plant Methods Image resolution Image sharpness Spectral accuracy Spatial accuracy Illumination Machine learning |
| title | Quality assurance of hyperspectral imaging systems for neural network supported plant phenotyping |
| title_full | Quality assurance of hyperspectral imaging systems for neural network supported plant phenotyping |
| title_fullStr | Quality assurance of hyperspectral imaging systems for neural network supported plant phenotyping |
| title_full_unstemmed | Quality assurance of hyperspectral imaging systems for neural network supported plant phenotyping |
| title_short | Quality assurance of hyperspectral imaging systems for neural network supported plant phenotyping |
| title_sort | quality assurance of hyperspectral imaging systems for neural network supported plant phenotyping |
| topic | Image resolution Image sharpness Spectral accuracy Spatial accuracy Illumination Machine learning |
| url | https://doi.org/10.1186/s13007-024-01315-y |
| work_keys_str_mv | AT justusdetring qualityassuranceofhyperspectralimagingsystemsforneuralnetworksupportedplantphenotyping AT abelbarreto qualityassuranceofhyperspectralimagingsystemsforneuralnetworksupportedplantphenotyping AT annekatrinmahlein qualityassuranceofhyperspectralimagingsystemsforneuralnetworksupportedplantphenotyping AT stefanpaulus qualityassuranceofhyperspectralimagingsystemsforneuralnetworksupportedplantphenotyping |