Open AccessSystematic Review
Machine Learning Techniques for Nematode Microscopic Image Analysis: A Systematic Review
by
Jose Luis Jimenez, Prem Gandhi, Devadharshini Ayyappan, Adrienne Gorny, Weimin Ye and Edgar Lobaton
AgriEngineering 2025, 7(11), 356; https://doi.org/10.3390/agriengineering7110356 (registering DOI) - 22 Oct 2025
Abstract
Farmers rely on nematode analysis for critical crop management decisions, yet traditional detection and classification methods remain subjective, labor-intensive, and time-consuming. Advances in Machine Learning (ML) and Deep Learning (DL) offer scalable solutions for automating microscopy-based nematode analyses. This systematic literature review (SLR)
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Farmers rely on nematode analysis for critical crop management decisions, yet traditional detection and classification methods remain subjective, labor-intensive, and time-consuming. Advances in Machine Learning (ML) and Deep Learning (DL) offer scalable solutions for automating microscopy-based nematode analyses. This systematic literature review (SLR) analyzed 44 articles published between 2018 and 2024 on ML/DL-based nematode image analysis, selected from 1460 records screened across Web of Science, IEEE Xplore, Agricola, and supplemental Google scholar searches. The quality of reporting was examined by considering dataset documentation and code availability. The results were synthesized narratively, as diversity in datasets, tasks, and metrics precluded a meta-analysis. Performance was primarily reported using accuracy, precision, recall, F1-score, Dice coefficient, Intersection over Union (IoU), and average precision (AP). CNNs were the most commonly used architectures, with models such as YOLO providing the best detection performance. Transformer-based models excelled in dense segmentation and counting. Despite strong performance, challenges include limited training data, occlusion, and inconsistent metric reporting across tasks. Although ML/DL models hold promise for scalable nematode analysis, future research should prioritize real-world validation, diverse nematode datasets, and standardized benchmark datasets to enable fair and reproducible model comparison.
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