AI and Machine Learning Based Detection of Nematode Disease in Plants
Abstract & Details
Research Area
Artificial Intelligence and Data Science
Keywords
Nematode disease detection
Artificial Intelligence
Machine Learning
Deep Learning
Precision Agriculture
Soil-based analysis
Root image processing
Leaf image classification
Plant disease detection
CNN
Sensor-based monitoring
Soil parameters
Multimodal data fusion
Early disease detection
Severity classification
Smart agriculture
Agricultural AI systems
Computer vision in agriculture
Abstract
Nematode infections significantly affect crop productivity by damaging plant root systems, yet early detection remains difficult due to the lack of visible symptoms in initial stages. This paper reviews the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques for nematode disease detection using multiple data sources, including leaf images, soil parameters, and root images. Existing approaches such as Convolutional Neural Networks (CNNs), sensor-based soil analysis, and image processing methods are analyzed for their effectiveness and limitations. The study highlights that single-source detection methods often fail to provide accurate early-stage diagnosis. To address this issue, a multi-modal and multi-stage detection framework is emphasized, enabling classification of disease severity into early, moderate, and severe levels. Furthermore, key research gaps such as limited datasets, lack of real-time adaptability, and poor field-level deployment are identified. The proposed integrated approach supports improved accuracy and sustainable precision agriculture practices.
License
This work is licensed under a Creative
Commons
Attribution-ShareAlike 4.0 International License.
Author Information
| # | Name | Institute / Affiliation |
|---|---|---|
| 1 | Nomeshvari Gaurkar | Priyadarshini college of Engineering, Nagpur |
| 2 | Nurjahat Choudhari | Priyadarshini college of Engineering, Nagpur |
| 3 | Darshan Ubhale | Priyadarshini college of Engineering, Nagpur |
| 4 | Samruddhi Bendre | Priyadarshini college of Engineering, Nagpur |
| 5 | Dr. Gajendra Asutkar | Priyadarshini college of Engineering, Nagpur |
How to Cite
Use the following formats to cite this article in your research.
APA Style
Gaurkar, Nomeshvari, Choudhari, Nurjahat, Ubhale, Darshan, Bendre, Samruddhi, & Asutkar, Dr. Gajendra (2026). AI and Machine Learning Based Detection of Nematode Disease in Plants. International Journal of Advance Research and Innovative Ideas In Education, 12(2), 1051-1055.
MLA Style
Gaurkar, Nomeshvari, et al. "AI and Machine Learning Based Detection of Nematode Disease in Plants." International Journal of Advance Research and Innovative Ideas In Education, vol. 12, no. 2, 2026, pp. 1051-1055.
IEEE Style
Nomeshvari Gaurkar, Nurjahat Choudhari, Darshan Ubhale, Samruddhi Bendre, and Dr. Gajendra Asutkar, "AI and Machine Learning Based Detection of Nematode Disease in Plants," International Journal of Advance Research and Innovative Ideas In Education, vol. 12, no. 2, pp. 1051-1055, 2026.
Vancouver Style
Gaurkar Nomeshvari, Choudhari Nurjahat, Ubhale Darshan, Bendre Samruddhi, Asutkar Dr. Gajendra. AI and Machine Learning Based Detection of Nematode Disease in Plants. International Journal of Advance Research and Innovative Ideas In Education. 2026;12(2):1051-1055.
Harvard Style
Gaurkar, Nomeshvari, Choudhari, Nurjahat, Ubhale, Darshan, Bendre, Samruddhi, & Asutkar, Dr. Gajendra (2026) 'AI and Machine Learning Based Detection of Nematode Disease in Plants', International Journal of Advance Research and Innovative Ideas In Education, 12(2), pp. 1051-1055.
Chicago Style
Gaurkar, Nomeshvari, et al. "AI and Machine Learning Based Detection of Nematode Disease in Plants." International Journal of Advance Research and Innovative Ideas In Education 12, no. 2 (2026): 1051-1055.
Turabian Style
Gaurkar, Nomeshvari, et al. "AI and Machine Learning Based Detection of Nematode Disease in Plants." International Journal of Advance Research and Innovative Ideas In Education 12, no. 2 (2026): 1051-1055.
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