A user-friendly software to accurately count and measure cysts from the parasitic nematode Heterodera glycines

• Published in: Scientific reports Vol. 15; no. 1; pp. 4468 - 12
• Main Authors: Mejias, Joffrey, Kozlowski, Djampa K. L., Goshon, Jackson, Maier, Thomas R., Baum, Thomas J.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 06.02.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1564; 631/449/2169/597; Animals; Cysts; Gene mapping; Genome-wide association studies; Glycine max - parasitology; Heterodera glycines; Humanities and Social Sciences; multidisciplinary; Nemacounter; Neural network; New varieties; Plant breeding; Plant Diseases - parasitology; Plant Roots - parasitology; Quantitative Trait Loci; Roots; Science; Science (multidisciplinary); Software; Soybean cyst nematode; Soybeans; Tylenchoidea; YOLOv5; Soybean cyst nematode; Neural network; YOLOv5; Nemacounter
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-88289-6

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.