Detection of Apple Proliferation Disease Using Hyperspectral Imaging and Machine Learning Techniques
Apple proliferation is among the most important diseases in European fruit production. Early and reliable detection enables farmers to respond appropriately and to prevent further spreading of the disease. Traditional phenotyping approaches by human observers consider multiple symptoms, but these ar...
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2024-12-01
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| Online Access: | https://www.mdpi.com/1424-8220/24/23/7774 |
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| author | Uwe Knauer Sebastian Warnemünde Patrick Menz Bonito Thielert Lauritz Klein Katharina Holstein Miriam Runne Wolfgang Jarausch |
| author_facet | Uwe Knauer Sebastian Warnemünde Patrick Menz Bonito Thielert Lauritz Klein Katharina Holstein Miriam Runne Wolfgang Jarausch |
| author_sort | Uwe Knauer |
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| description | Apple proliferation is among the most important diseases in European fruit production. Early and reliable detection enables farmers to respond appropriately and to prevent further spreading of the disease. Traditional phenotyping approaches by human observers consider multiple symptoms, but these are difficult to measure automatically in the field. Therefore, the potential of hyperspectral imaging in combination with data analysis by machine learning algorithms was investigated to detect the symptoms solely based on the spectral signature of collected leaf samples. In the growing seasons 2019 and 2020, a total of 1160 leaf samples were collected. Hyperspectral imaging with a dual camera setup in spectral bands from 400 nm to 2500 nm was accompanied with subsequent PCR analysis of the samples to provide reference data for the machine learning approaches. Data processing consists of preprocessing for segmentation of the leaf area, feature extraction, classification and subsequent analysis of relevance of spectral bands. The results show that imaging multiple leaves of a tree enhances detection results, that spectral indices are a robust means to detect the diseased trees, and that the potentials of the full spectral range can be exploited using machine learning approaches. Classification models like rRBF achieved an accuracy of 0.971 in a controlled environment with stratified data for a single variety. Combined models for multiple varieties from field test samples achieved classification accuracies of 0.731. Including spatial distribution of spectral data further improves the results to 0.751. Prediction of qPCR results by regression based on spectral data achieved RMSE of 14.491 phytoplasma per plant cell. |
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| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-9d41a9e49dce43b5bc936eb2bcec54022024-12-13T16:32:44ZengMDPI AGSensors1424-82202024-12-012423777410.3390/s24237774Detection of Apple Proliferation Disease Using Hyperspectral Imaging and Machine Learning TechniquesUwe Knauer0Sebastian Warnemünde1Patrick Menz2Bonito Thielert3Lauritz Klein4Katharina Holstein5Miriam Runne6Wolfgang Jarausch7Department of Agriculture, Ecotrophology and Landscape Development, Anhalt University of Applied Sciences, 06406 Bernburg, GermanyCognitive Processes and Systems, Fraunhofer Institute for Factory Operation and Automation IFF, 39106 Magdeburg, GermanyCognitive Processes and Systems, Fraunhofer Institute for Factory Operation and Automation IFF, 39106 Magdeburg, GermanyCognitive Processes and Systems, Fraunhofer Institute for Factory Operation and Automation IFF, 39106 Magdeburg, GermanyCognitive Processes and Systems, Fraunhofer Institute for Factory Operation and Automation IFF, 39106 Magdeburg, GermanyDepartment of Computer Science and Languages, Anhalt University of Applied Sciences, 06366 Köthen, GermanyRLP AgroScience, 67435 Neustadt an der Weinstrasse, GermanyRLP AgroScience, 67435 Neustadt an der Weinstrasse, GermanyApple proliferation is among the most important diseases in European fruit production. Early and reliable detection enables farmers to respond appropriately and to prevent further spreading of the disease. Traditional phenotyping approaches by human observers consider multiple symptoms, but these are difficult to measure automatically in the field. Therefore, the potential of hyperspectral imaging in combination with data analysis by machine learning algorithms was investigated to detect the symptoms solely based on the spectral signature of collected leaf samples. In the growing seasons 2019 and 2020, a total of 1160 leaf samples were collected. Hyperspectral imaging with a dual camera setup in spectral bands from 400 nm to 2500 nm was accompanied with subsequent PCR analysis of the samples to provide reference data for the machine learning approaches. Data processing consists of preprocessing for segmentation of the leaf area, feature extraction, classification and subsequent analysis of relevance of spectral bands. The results show that imaging multiple leaves of a tree enhances detection results, that spectral indices are a robust means to detect the diseased trees, and that the potentials of the full spectral range can be exploited using machine learning approaches. Classification models like rRBF achieved an accuracy of 0.971 in a controlled environment with stratified data for a single variety. Combined models for multiple varieties from field test samples achieved classification accuracies of 0.731. Including spatial distribution of spectral data further improves the results to 0.751. Prediction of qPCR results by regression based on spectral data achieved RMSE of 14.491 phytoplasma per plant cell.https://www.mdpi.com/1424-8220/24/23/7774hyperspectralrandom forestmachine learningapple‘<i>Candidatus</i> Phytoplasma mali’disease monitoring |
| spellingShingle | Uwe Knauer Sebastian Warnemünde Patrick Menz Bonito Thielert Lauritz Klein Katharina Holstein Miriam Runne Wolfgang Jarausch Detection of Apple Proliferation Disease Using Hyperspectral Imaging and Machine Learning Techniques Sensors hyperspectral random forest machine learning apple ‘<i>Candidatus</i> Phytoplasma mali’ disease monitoring |
| title | Detection of Apple Proliferation Disease Using Hyperspectral Imaging and Machine Learning Techniques |
| title_full | Detection of Apple Proliferation Disease Using Hyperspectral Imaging and Machine Learning Techniques |
| title_fullStr | Detection of Apple Proliferation Disease Using Hyperspectral Imaging and Machine Learning Techniques |
| title_full_unstemmed | Detection of Apple Proliferation Disease Using Hyperspectral Imaging and Machine Learning Techniques |
| title_short | Detection of Apple Proliferation Disease Using Hyperspectral Imaging and Machine Learning Techniques |
| title_sort | detection of apple proliferation disease using hyperspectral imaging and machine learning techniques |
| topic | hyperspectral random forest machine learning apple ‘<i>Candidatus</i> Phytoplasma mali’ disease monitoring |
| url | https://www.mdpi.com/1424-8220/24/23/7774 |
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