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Semi-supervised few-shot learning approach for plant diseases recognition
Yang Li1,2, Xuewei Chao3
1College of Mechanical and Electrical Engineering, Shihezi University, Xinjiang, China.
Plant Methods
|June 28, 2021
Summary
This study introduces a semi-supervised few-shot learning approach for plant leaf disease recognition, improving accuracy by leveraging unlabeled data. The novel method enhances agricultural yield protection with fewer labeled samples.
Area of Science:
- Agricultural Science
- Computer Vision
- Machine Learning
Background:
- Few-shot classification for plant leaf disease recognition is crucial for agricultural yield.
- Existing methods often overlook valuable unlabeled data in agricultural datasets.
Purpose of the Study:
- To develop a semi-supervised few-shot learning approach for accurate plant leaf disease recognition.
- To enhance agricultural protection by effectively utilizing limited labeled samples and abundant unlabeled data.
Main Methods:
- Proposed a semi-supervised few-shot learning framework using the PlantVillage dataset, split into source and target domains.
- Conducted extensive experiments to validate the approach's correctness and generalization across different domain splits and few-shot parameters (N-way, k-shot).
- Employed a confidence interval method for adaptive pseudo-labeling of unlabeled samples within the semi-supervised process.
Main Results:
- The single semi-supervised method achieved an average improvement of 2.8%.
- The iterative semi-supervised method demonstrated an average improvement of 4.6%.
- The proposed methods showed superior performance compared to existing related works.
Conclusions:
- The developed semi-supervised few-shot learning methods effectively improve plant leaf disease recognition accuracy.
- These methods offer a significant advantage by requiring fewer labeled training samples, making them practical for real-world agricultural applications.
