ArticleFoods (Basel, Switzerland)2021
Meta-Learning for Few-Shot Plant Disease Detection.
Article in Foods (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Plant Disease Detection and Classification: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Cross Disease Similarity Awareness Learning (CDSAL) with DenseNet-EfficientNet embedding fusion for high-precision tomato leaf pathology classification with Grad-CAM explainability.Scientific reports · 2026Article
- An explainable deep learning framework for few shot crop disease detection in rice and sugarcane using CNN based feature extraction.Scientific reports · 2026Article
- Few-shot object detection for pest insects via features aggregation and contrastive learning.Frontiers in plant science · 2025Article
- Few-shot disease recognition algorithm based on supervised contrastive learning.Frontiers in plant science · 2024Article
- From laboratory to field: cross-domain few-shot learning for crop disease identification in the field.Frontiers in plant science · 2024Article
- Plant and Disease Recognition Based on PMF Pipeline Domain Adaptation Method: Using Bark Images as Meta-Dataset.Plants (Basel, Switzerland) · 2023Article
- Embracing limited and imperfect training datasets: opportunities and challenges in plant disease recognition using deep learning.Frontiers in plant science · 2023Article
- Few-Shot Learning for Plant-Disease Recognition in the Frequency Domain.Plants (Basel, Switzerland) · 2022Article
- Challenging the Status Quo to Shape Food Systems Transformation from a Nutritional and Food Security Perspective.Foods (Basel, Switzerland) · 2022Article
- Few-shot learning approach with multi-scale feature fusion and attention for plant disease recognition.Frontiers in plant science · 2022Article
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Authors and funding
3 authors.
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Abstract
Plant diseases can harm crop growth, and the crop production has a deep impact on food. Although the existing works adopt Convolutional Neural Networks (CNNs) to detect plant diseases such as Apple Scab and Squash Powdery mildew, those methods have limitations as they rely on a large amount of manually labeled data. Collecting enough labeled data is not often the case in practice because: plant pathogens are variable and farm environments make collecting data difficulty. Methods based on deep learning suffer from low accuracy and confidence when facing few-shot samples. In this paper, we propose local feature matching conditional neural adaptive processes (LFM-CNAPS) based on meta-learning that aims at detecting plant diseases of unseen categories with only a few annotated examples, and visualize input regions that are 'important' for predictions. To train our network, we contribute Miniplantdisease-Dataset that contains 26 plant species and 60 plant diseases. Comprehensive experiments demonstrate that our proposed LFM-CNAPS method outperforms the existing methods.
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