A user-friendly software to accurately count and measure cysts from the parasitic nematode Heterodera glycines
Abstract The soybean-cyst nematode (SCN; Heterodera glycines) is one of the most destructive pests affecting soybean crops. Effective management of SCN is imperative for the sustainability of soybean agriculture. A promising approach to achieving this goal is the development and breeding of new resi...
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88289-6 |
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| author | Joffrey Mejias Djampa K. L. Kozlowski Jackson Goshon Thomas R. Maier Thomas J. Baum |
| author_facet | Joffrey Mejias Djampa K. L. Kozlowski Jackson Goshon Thomas R. Maier Thomas J. Baum |
| author_sort | Joffrey Mejias |
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| description | Abstract The soybean-cyst nematode (SCN; Heterodera glycines) is one of the most destructive pests affecting soybean crops. Effective management of SCN is imperative for the sustainability of soybean agriculture. A promising approach to achieving this goal is the development and breeding of new resistant soybean varieties. Researchers and breeders typically employ exploratory methods such as Genome-Wide Association Studies or Quantitative Trait Loci mapping to identify genes linked to resistance. These methods depend on extensive phenotypic screening. The primary phenotypic measure for assessing SCN resistance is often the number of cysts that form on a plant’s root system. Manual counting hundreds of cysts on a given root system is not only laborious but also subject to variability due to individual assessor differences. Additionally, while measuring cyst size could provide valuable insights due to its correlation with cyst development, this aspect is frequently overlooked because it demands even more hands-on work. To address these challenges, we have created Nemacounter, an intuitive software designed to detect, count, and measure the size of cysts autonomously. Nemacounter boasts a user-friendly graphical interface, simplifying the process for users to obtain reliable results. It enhances productivity by delivering annotated images and compiling data into csv files for easy analysis and reporting. |
| format | Article |
| id | doaj-art-9247300b5e7246c88dcf2094b6d5c997 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-9247300b5e7246c88dcf2094b6d5c9972025-02-09T12:33:45ZengNature PortfolioScientific Reports2045-23222025-02-0115111210.1038/s41598-025-88289-6A user-friendly software to accurately count and measure cysts from the parasitic nematode Heterodera glycinesJoffrey Mejias0Djampa K. L. Kozlowski1Jackson Goshon2Thomas R. Maier3Thomas J. Baum4Department of Plant Pathology, Entomology and Microbiology, Iowa State UniversityINRAE, Université Côte d’Azur, CNRS, Institut Sophia AgrobiotechDepartment of Plant Pathology, Entomology and Microbiology, Iowa State UniversityDepartment of Plant Pathology, Entomology and Microbiology, Iowa State UniversityDepartment of Plant Pathology, Entomology and Microbiology, Iowa State UniversityAbstract The soybean-cyst nematode (SCN; Heterodera glycines) is one of the most destructive pests affecting soybean crops. Effective management of SCN is imperative for the sustainability of soybean agriculture. A promising approach to achieving this goal is the development and breeding of new resistant soybean varieties. Researchers and breeders typically employ exploratory methods such as Genome-Wide Association Studies or Quantitative Trait Loci mapping to identify genes linked to resistance. These methods depend on extensive phenotypic screening. The primary phenotypic measure for assessing SCN resistance is often the number of cysts that form on a plant’s root system. Manual counting hundreds of cysts on a given root system is not only laborious but also subject to variability due to individual assessor differences. Additionally, while measuring cyst size could provide valuable insights due to its correlation with cyst development, this aspect is frequently overlooked because it demands even more hands-on work. To address these challenges, we have created Nemacounter, an intuitive software designed to detect, count, and measure the size of cysts autonomously. Nemacounter boasts a user-friendly graphical interface, simplifying the process for users to obtain reliable results. It enhances productivity by delivering annotated images and compiling data into csv files for easy analysis and reporting.https://doi.org/10.1038/s41598-025-88289-6Neural networkYOLOv5Soybean cyst nematodeNemacounter |
| spellingShingle | Joffrey Mejias Djampa K. L. Kozlowski Jackson Goshon Thomas R. Maier Thomas J. Baum A user-friendly software to accurately count and measure cysts from the parasitic nematode Heterodera glycines Scientific Reports Neural network YOLOv5 Soybean cyst nematode Nemacounter |
| title | A user-friendly software to accurately count and measure cysts from the parasitic nematode Heterodera glycines |
| title_full | A user-friendly software to accurately count and measure cysts from the parasitic nematode Heterodera glycines |
| title_fullStr | A user-friendly software to accurately count and measure cysts from the parasitic nematode Heterodera glycines |
| title_full_unstemmed | A user-friendly software to accurately count and measure cysts from the parasitic nematode Heterodera glycines |
| title_short | A user-friendly software to accurately count and measure cysts from the parasitic nematode Heterodera glycines |
| title_sort | user friendly software to accurately count and measure cysts from the parasitic nematode heterodera glycines |
| topic | Neural network YOLOv5 Soybean cyst nematode Nemacounter |
| url | https://doi.org/10.1038/s41598-025-88289-6 |
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