Identification of plant-parasitic nematode genera in turfgrass using deep learning algorithms.

ABSTRACT: Plant-parasitic nematodes are an important threat to turfgrass. Left unmanaged, they can cause serious reductions in the quality and playability of golf courses and sports fields. Effective nematode management depends on accurate identification of the nematode genera extracted from soil samples. However, this process requires specialized expertise in nematology, which is often limited in plant diagnostic laboratories. Recent advancements in deep learning models offer promising solutions for the future of nematode identification. In this study, we evaluated the performance of EfficientNet V2-S, MobileNetV3-L, ResNet101, and Swin Transformer V2-B convolutional neural network model architectures in the classification of seven nematode taxa associated with turfgrass. Models were trained using

SUBMITTER: Rangarajan V 

PROVIDER: S-EPMC12764819 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature