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Journal article
A transformer-based few-shot learning pipeline for barley disease detection from field-collected imagery
Computers and electronics in agriculture, Vol.229, 109751
2025
Abstract
Artificial intelligence (AI) and deep learning (DL) for plant disease detection are emerging research areas. DL methods generally require a large amount of annotated data for training, which is often costly, time-consuming, and infeasible. This article addresses the data scarcity problem and proposes a few-shot learning (FSL) method for barley plant disease detection. To prepare a dataset, we captured images from outdoor test-bed trials (at two different growth stages of plants across multiple paddocks) under various weather conditions, such as sunny and cloudy. The images are divided into patches and manually labelled into five classes: no-disease, net form net blotch (NFNB) (which is classified into two stages: early and severe), spot form net blotch (SFNB), and scald. We name this as the Barley dataset. We also used the publicly available cassava dataset, which has five classes. The datasets are then applied to the proposed FSL pipeline, which only uses as few as five images for each class in training. We use the Swin transformer as the network backbone. The method with the Swin-B variant as the feature extractor achieved a detection accuracy of 91.80% and 97.93% on the barley disease and cassava datasets, respectively. The result indicates that our FSL model can efficiently perform and classify barley disease with small training data.
•A few-shot learning method is developed to address data scarcity problems.•Results on the collected plant disease data warrant the model’s potential.•Cutting-edge transformers, e.g. Swin-B, perform well given only five training images•Meta-training and transfer learning significantly improve performance.•Apparent disease symptoms can be detected and used in various applications.
Details
- Title
- A transformer-based few-shot learning pipeline for barley disease detection from field-collected imagery
- Authors/Creators
- Masoud Rezaei - Murdoch UniversityDean Diepeveen - Murdoch University, School of Information TechnologyHamid Laga - Murdoch University, Centre for Biosecurity and One HealthSanjiv Gupta - Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, WA 6150, AustraliaMichael G.K. Jones - Murdoch University, Centre for Crop and Food InnovationFerdous Sohel - Murdoch University, Centre for Crop and Food Innovation
- Publication Details
- Computers and electronics in agriculture, Vol.229, 109751
- Publisher
- Elsevier B.V.
- Number of pages
- 11
- Grant note
- Murdoch University We acknowledge the financial support provided by Murdoch University strategic research scholarships. We also acknowledge Simon Rogers (Lab Technician, DPIRD) for running the disease trials.
- Identifiers
- 991005723865007891
- Copyright
- © 2024 The Authors.
- Murdoch Affiliation
- Centre for Biosecurity and One Health; Centre for Crop and Food Innovation; School of Information Technology; Centre for Healthy Ageing
- Language
- English
- Resource Type
- Journal article
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- Collaboration types
- Domestic collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.17 Computer Vision & Graphics
- 4.17.128 Deep Visual Recognition
- Web Of Science research areas
- Agriculture, Multidisciplinary
- Computer Science, Interdisciplinary Applications
- ESI research areas
- Computer Science