Nematode Detection and Classification Using Machine Learning Techniques: A Review Article Swipe
Arjun Neupane
,
Tej Bahadur Shahi
,
Richard Koech
,
Kerry B. Walsh
,
Philip Kibet Langat
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/agronomy15112481
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/agronomy15112481
Nematode identification and quantification are critical for understanding their impact on agricultural ecosystems. However, traditional methods rely on specialised expertise in nematology, making the process costly and time-consuming. Recent developments in technologies such as Artificial Intelligence (AI) and computer vision (CV) offer promising alternatives for automating nematode identification and counting at scale. This work reviews the current literature on nematode detection using AI techniques, focusing on their application, performance, and limitations. First, we discuss various image analysis, machine learning (ML), and deep learning (DL) methods, including You Only Look Once (YOLO) models, and evaluate their effectiveness in detecting and classifying nematodes. Second, we compare and contrast the performance of ML- and DL-based approaches on different nematode datasets. Next, we highlight how these techniques can support sustainable agricultural practices and optimise crop productivity. Finally, we conclude by outlining the key opportunities and challenges in integrating ML and DL methods for precise and efficient nematode management.
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2025Year of publication
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2025-10-25Full publication date if available
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Arjun Neupane, Tej Bahadur Shahi, Richard Koech, Kerry B. Walsh, Philip Kibet LangatList of authors in order
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https://doi.org/10.3390/agronomy15112481Publisher landing page
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goldOpen access status per OpenAlex
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0Total citation count in OpenAlex
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79Number of works referenced by this work
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| abstract_inverted_index.different | 114 |
| abstract_inverted_index.efficient | 151 |
| abstract_inverted_index.expertise | 19 |
| abstract_inverted_index.highlight | 119 |
| abstract_inverted_index.including | 85 |
| abstract_inverted_index.outlining | 136 |
| abstract_inverted_index.practices | 127 |
| abstract_inverted_index.promising | 42 |
| abstract_inverted_index.Artificial | 34 |
| abstract_inverted_index.approaches | 112 |
| abstract_inverted_index.automating | 45 |
| abstract_inverted_index.challenges | 141 |
| abstract_inverted_index.literature | 57 |
| abstract_inverted_index.nematodes. | 100 |
| abstract_inverted_index.techniques | 122 |
| abstract_inverted_index.classifying | 99 |
| abstract_inverted_index.ecosystems. | 12 |
| abstract_inverted_index.integrating | 143 |
| abstract_inverted_index.management. | 153 |
| abstract_inverted_index.nematology, | 21 |
| abstract_inverted_index.performance | 107 |
| abstract_inverted_index.specialised | 18 |
| abstract_inverted_index.sustainable | 125 |
| abstract_inverted_index.techniques, | 63 |
| abstract_inverted_index.traditional | 14 |
| abstract_inverted_index.Intelligence | 35 |
| abstract_inverted_index.agricultural | 11, 126 |
| abstract_inverted_index.alternatives | 43 |
| abstract_inverted_index.application, | 67 |
| abstract_inverted_index.developments | 29 |
| abstract_inverted_index.limitations. | 70 |
| abstract_inverted_index.performance, | 68 |
| abstract_inverted_index.technologies | 31 |
| abstract_inverted_index.effectiveness | 95 |
| abstract_inverted_index.opportunities | 139 |
| abstract_inverted_index.productivity. | 131 |
| abstract_inverted_index.understanding | 7 |
| abstract_inverted_index.identification | 1, 47 |
| abstract_inverted_index.quantification | 3 |
| abstract_inverted_index.time-consuming. | 27 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.35808069 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |