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Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

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Few-Shot Learning for Plant-Disease Recognition in the Frequency Domain.

Hong Lin¹, Rita Tse¹, Su-Kit Tang¹

¹Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR 999078, China.

Plants (Basel, Switzerland)

|November 11, 2022

Summary

Few-shot learning for plant disease recognition benefits from frequency domain analysis. New methods improve feature representation and generalization, outperforming spatial domain approaches for better agricultural applications.

Keywords:

Gaussian-like calibration discrete cosine transform few-shot learning frequency domain plant disease recognition power transform

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Area of Science:

Computer Vision
Machine Learning
Plant Pathology

Background:

Few-shot learning (FSL) is crucial for plant disease recognition due to limited data.
Existing methods struggle with feature representation and generalization in spatial domains.
Frequency domain analysis offers rich patterns for image understanding.

Purpose of the Study:

To introduce frequency representation into FSL for plant disease recognition.
To enhance feature representation and generalization capabilities.
To develop adaptable modules for existing networks.

Main Methods:

Utilized Discrete Cosine Transform (DCT) to convert RGB images to the frequency domain.
Developed a learning-based frequency selection method for informative frequencies.
Implemented a Gaussian-like calibration module for improved generalization.

Main Results:

Frequency domain analysis significantly outperformed spatial domain methods.
The Gaussian-like calibrator further enhanced model performance.
Proposed modules demonstrated effectiveness and portability.

Conclusions:

Frequency domain representation is highly effective for FSL plant disease recognition.
The proposed DCT and calibration modules offer robust solutions for feature enhancement and generalization.
Future work includes cross-domain disease identification for agricultural applications.