ArticleFrontiers in plant science2022
Transfer learning for versatile plant disease recognition with limited data.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- From detection to action: artificial intelligence in integrated pest and invasive plant management.Advanced biotechnology · 2026Review
- Fast Forward the Future: What Are the Key Drivers in Intelligent Sensing for Agriculture?Plants (Basel, Switzerland) · 2026Article
- RGB-Based Deep Learning for Freeze Damage Detection in Strawberry: Comparing Scratch and Transfer Learning Approaches on Custom Data.Plant direct · 2025Article
- A Review on the Detection of Plant Disease Using Machine Learning and Deep Learning Approaches.Journal of imaging · 2025Review
- Artificial Intelligence-Assisted Breeding for Plant Disease Resistance.International journal of molecular sciences · 2025Review
- Using AI to Differentiate Mpox From Common Skin Lesions in a Sexual Health Clinic: Algorithm Development and Validation Study.Journal of medical Internet research · 2024Article
- Evaluation of artificial intelligence-powered screening for sexually transmitted infections-related skin lesions using clinical images and metadata.BMC medicine · 2024Article
- AI can empower agriculture for global food security: challenges and prospects in developing nations.Frontiers in artificial intelligence · 2024Review
- Plant disease recognition datasets in the age of deep learning: challenges and opportunities.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
- Improving Known-Unknown Cattle's Face Recognition for Smart Livestock Farm Management.Animals : an open access journal from MDPI · 2023Article
- Crop-saving with AI: latest trends in deep learning techniques for plant pathology.Frontiers in plant science · 2023Review
- Embracing limited and imperfect training datasets: opportunities and challenges in plant disease recognition using deep learning.Frontiers in plant science · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning has witnessed a significant improvement in recent years to recognize plant diseases by observing their corresponding images. To have a decent performance, current deep learning models tend to require a large-scale dataset. However, collecting a dataset is expensive and time-consuming. Hence, the limited data is one of the main challenges to getting the desired recognition accuracy. Although transfer learning is heavily discussed and verified as an effective and efficient method to mitigate the challenge, most proposed methods focus on one or two specific datasets. In this paper, we propose a novel transfer learning strategy to have a high performance for
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