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

Authors: RANGARAJAN, VIKRAM - University Of Maryland; SHAHOVEISI, FERESHTEH - University Of Maryland; Waldo, Benjamin; JAFARI, SADEGH - Iowa State University

Submitted to: Scientific Reports
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/15/2025
Publication Date: 12/7/2025
Citation: Rangarajan, V., Shahoveisi, F., Waldo, B.D., Jafari, S. 2025. Identification of plant-parasitic nematode genera in turfgrass using deep learning algorithms. Scientific Reports. 16. Article 24. https://doi.org/10.1038/s41598-025-29467-4.
DOI: https://doi.org/10.1038/s41598-025-29467-4

Interpretive Summary: Plant-parasitic nematodes are microscopic pathogens of many crops, including turfgrass. They can inflict injury to plant roots which can result in visual decline or death of turfgrass. Since nematodes are microscopic, reliable detection and diagnostics requires examination of samples extracted from soil using a microscope. Identification of plant-parasitic nematodes is challenging and generally limited to experts in specialized laboratories. Machine learning image classification models provide a unique opportunity for enhancing diagnostics of plant-parasitic nematodes. In this study a machine learning model capable of differentiating 7 common nematodes associated with turfgrass was developed. The machine learning model and associated database serves as a primary building block to develop a comprehensive a diagnostics system. This system aims to help increase accessibility of nematode diagnostics to plant diagnostics laboratories and increase efficiency of existing nematode diagnostic laboratories.

Technical 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 a dataset of 5406 plant-parasitic nematode images where the dataset was split into 70, 15, and 15% for training, testing, and validation, respectively. Data augmentation and hyperparameter optimization using a combined Bayesian optimization and Hyperband algorithm (BOHB) approach were used to improve the model performance. Balanced classification accuracy on the test set was highest for EfficientNet V2-S and Swin Transformer V2-B at 94.63% and 94.34%, respectively. MobileNetV3-L and ResNet101 had lower balanced accuracies of 90.83% and 86.33%, respectively. Testing the models on an additional dataset using a user-end platform indicated the superiority of EfficientNet V2-S to other models with 82.47% accuracy. The findings of this study indicate the potential application of deep learning tools for accurate nematode identification to aid in diagnostics.