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Related Experiment Video

Updated: May 21, 2026

Remote Sensing Evaluation of Two-spotted Spider Mite Damage on Greenhouse Cotton

Remote Sensing Evaluation of Two-spotted Spider Mite Damage on Greenhouse Cotton

Published on: April 28, 2017

Classification of Rotylenchulus reniformis Numbers in Cotton Using Remotely Sensed Hyperspectral Data on

Rushabh A Doshi¹, Roger L King, Gary W Lawrence

¹Graduate Student, Department of Electrical and Computer Engineering.

Journal of Nematology

|June 28, 2012

Summary

Early detection of Reniform nematodes (Rotylenchulus reniformis) in cotton is crucial for yield protection. Hyperspectral imaging and self-organizing maps (SOM) show promise for classifying nematode populations, enabling timely intervention.

Keywords:

Classification Gossypium hirsutum Rotylenchulus reniformis Self-Organized Maps cotton nematode

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Published on: October 11, 2016

Area of Science:

Agricultural remote sensing
Plant pathology
Data science

Background:

Reniform nematodes (Rotylenchulus reniformis) cause significant cotton yield losses, exceeding millions of dollars.
Early detection of nematode infestation is vital for effective crop management and economic loss mitigation.

Purpose of the Study:

To assess the feasibility of using hyperspectral reflectance data from cotton plants to detect and classify varying population densities of Reniform nematodes.
To correlate plant hyperspectral signatures with nematode population numbers in the rhizosphere.

Main Methods:

Acquisition of hyperspectral data (350-2500 nm) from cotton plants under different nematode infestation levels.
Application of feature extraction and dimensionality reduction techniques on Visible, Red Edge + Near Infrared (NIR), and Mid-Infrared (Mid-IR) spectral regions.
Classification of nematode population numbers using supervised self-organizing maps (SOM).

Main Results:

Classification accuracies ranged from 60% to 80% in most spectral regions (excluding the visible spectrum).
A positive correlation was observed between Reniform nematode population density and cotton plant hyperspectral signatures.
The Mid-IR region demonstrated classification accuracies comparable to other spectral sub-regions.

Conclusions:

Remotely sensed hyperspectral data, when analyzed with self-organizing maps (SOM), offers a time-efficient method for detecting Reniform nematode populations in cotton.
This approach has the potential to significantly aid in agricultural pest management strategies.