![]() Five representative images of CBB infected leaves from different DPI were selected and combined into one graphic as a training image for the machine_learning tool (Fig 3A). The authors sought to develop a machine_learning tool that would provide faster segmentation and quantification of diseased leaves. Some image analysis methods have incorporated machine_learning techniques for improved trait identification, classification, and faster analysis of plant disease symptoms (Singh, Tsaftaris ). In future studies if this approach were to be applied to datasets derived from multiple genotypes or a breeding program the classifier file would need to be updated with representative images to capture any additional variability in leaf traits. future: More cost effective few-shot image analysis tools that allow for efficient segmentation and quantification of disease symptoms are needed.To quantify CBB the authors developed and compared ImageJ and machine_learning image analysis methods for accurate segmentation and quantification of water-soaked lesion symptoms. how: The authors developed two image analysis and tested their ability to quantify different aspects of disease lesions in the cassava-Xanthomonas pathosystem.what: For this study, the authors designated red as Xam668, green as Xam668u0394TAL20, and blue as mock inoculation spots.who: Kiona Elliott from the (UNIVERSITY) have published the Article: A comparison of ImageJ and machine learning based image analysis methods to measure cassava bacterial blight disease severity, in the Journal: (JOURNAL). ![]()
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