The Ear Unwrapper: A Maize Ear Image Acquisition Pipeline for Disease Severity Phenotyping
Fusarium ear rot (FER) is a common disease in maize caused by the pathogen <i>Fusarium verticillioides</i>. Because of the quantitative nature of the disease, scoring disease severity is difficult and nuanced, relying on various ways to quantify the damage caused by the pathogen. Towards...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | AgriEngineering |
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| Online Access: | https://www.mdpi.com/2624-7402/5/3/77 |
| _version_ | 1797581703886143488 |
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| author | Owen Hudson Dylan Hudson Colin Brahmstedt Jeremy Brawner |
| author_facet | Owen Hudson Dylan Hudson Colin Brahmstedt Jeremy Brawner |
| author_sort | Owen Hudson |
| collection | DOAJ |
| description | Fusarium ear rot (FER) is a common disease in maize caused by the pathogen <i>Fusarium verticillioides</i>. Because of the quantitative nature of the disease, scoring disease severity is difficult and nuanced, relying on various ways to quantify the damage caused by the pathogen. Towards the goal of designing a system with greater objectivity, reproducibility, and accuracy than subjective scores or estimations of the infected area, a system of semi-automated image acquisition and subsequent image analysis was designed. The tool created for image acquisition, “The Ear Unwrapper”, successfully obtained images of the full exterior of maize ears. A set of images produced from The Ear Unwrapper was then used as an example of how machine learning could be used to estimate disease severity from unannotated images. A high correlation (0.74) was found between the methods estimating the area of disease, but low correlations (0.47 and 0.28) were found between the number of infected kernels and the area of disease, indicating how different methods can result in contrasting severity scores. This study provides an example of how a simplified image acquisition tool can be built and incorporated into a machine learning pipeline to measure phenotypes of interest. We also present how the use of machine learning in image analysis can be adapted from open-source software to estimate complex phenotypes such as Fusarium ear rot. |
| first_indexed | 2024-03-10T23:08:22Z |
| format | Article |
| id | doaj.art-4c743aa14777456497ad8ebea332d201 |
| institution | Directory Open Access Journal |
| issn | 2624-7402 |
| language | English |
| last_indexed | 2024-03-10T23:08:22Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AgriEngineering |
| spelling | doaj.art-4c743aa14777456497ad8ebea332d2012023-11-19T09:08:22ZengMDPI AGAgriEngineering2624-74022023-07-01531216122510.3390/agriengineering5030077The Ear Unwrapper: A Maize Ear Image Acquisition Pipeline for Disease Severity PhenotypingOwen Hudson0Dylan Hudson1Colin Brahmstedt2Jeremy Brawner3Department of Plant Pathology, The University of Florida, Fifield Hall, 2550 Hull Rd., Gainesville, FL 32611, USAIndependent Researcher, Boulder, CO 80303, USARev2 Industries, 88 Pima Ct, Boulder, CO 80303, USADepartment of Plant Pathology, The University of Florida, Fifield Hall, 2550 Hull Rd., Gainesville, FL 32611, USAFusarium ear rot (FER) is a common disease in maize caused by the pathogen <i>Fusarium verticillioides</i>. Because of the quantitative nature of the disease, scoring disease severity is difficult and nuanced, relying on various ways to quantify the damage caused by the pathogen. Towards the goal of designing a system with greater objectivity, reproducibility, and accuracy than subjective scores or estimations of the infected area, a system of semi-automated image acquisition and subsequent image analysis was designed. The tool created for image acquisition, “The Ear Unwrapper”, successfully obtained images of the full exterior of maize ears. A set of images produced from The Ear Unwrapper was then used as an example of how machine learning could be used to estimate disease severity from unannotated images. A high correlation (0.74) was found between the methods estimating the area of disease, but low correlations (0.47 and 0.28) were found between the number of infected kernels and the area of disease, indicating how different methods can result in contrasting severity scores. This study provides an example of how a simplified image acquisition tool can be built and incorporated into a machine learning pipeline to measure phenotypes of interest. We also present how the use of machine learning in image analysis can be adapted from open-source software to estimate complex phenotypes such as Fusarium ear rot.https://www.mdpi.com/2624-7402/5/3/77semi-automated image acquisitiondisease severity phenotypingmachine learningFusarium ear rotmaize |
| spellingShingle | Owen Hudson Dylan Hudson Colin Brahmstedt Jeremy Brawner The Ear Unwrapper: A Maize Ear Image Acquisition Pipeline for Disease Severity Phenotyping AgriEngineering semi-automated image acquisition disease severity phenotyping machine learning Fusarium ear rot maize |
| title | The Ear Unwrapper: A Maize Ear Image Acquisition Pipeline for Disease Severity Phenotyping |
| title_full | The Ear Unwrapper: A Maize Ear Image Acquisition Pipeline for Disease Severity Phenotyping |
| title_fullStr | The Ear Unwrapper: A Maize Ear Image Acquisition Pipeline for Disease Severity Phenotyping |
| title_full_unstemmed | The Ear Unwrapper: A Maize Ear Image Acquisition Pipeline for Disease Severity Phenotyping |
| title_short | The Ear Unwrapper: A Maize Ear Image Acquisition Pipeline for Disease Severity Phenotyping |
| title_sort | ear unwrapper a maize ear image acquisition pipeline for disease severity phenotyping |
| topic | semi-automated image acquisition disease severity phenotyping machine learning Fusarium ear rot maize |
| url | https://www.mdpi.com/2624-7402/5/3/77 |
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