Evaluation of Different Few-Shot Learning Methods in the Plant Disease Classification Domain
Early detection of plant diseases is crucial for agro-holdings, farmers, and smallholders. Various neural network architectures and training methods have been employed to identify optimal solutions for plant disease classification. However, research applying one-shot or few-shot learning approaches,...
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MDPI AG
2025-01-01
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| author | Alexander Uzhinskiy |
| author_facet | Alexander Uzhinskiy |
| author_sort | Alexander Uzhinskiy |
| collection | DOAJ |
| description | Early detection of plant diseases is crucial for agro-holdings, farmers, and smallholders. Various neural network architectures and training methods have been employed to identify optimal solutions for plant disease classification. However, research applying one-shot or few-shot learning approaches, based on similarity determination, to the plantdisease classification domain remains limited. This study evaluates different loss functions used in similarity learning, including Contrastive, Triplet, Quadruplet, SphereFace, CosFace, and ArcFace, alongside various backbone networks, such as MobileNet, EfficientNet, ConvNeXt, and ResNeXt. Custom datasets of real-life images, comprising over 4000 samples across 68 classes of plant diseases, pests, and their effects, were utilized. The experiments evaluate standard transfer learning approaches alongside similarity learning methods based on two classes of loss function. Results demonstrate the superiority of cosine-based methods over Siamese networks in embedding extraction for disease classification. Effective approaches for model organization and training are determined. Additionally, the impact of data normalization is tested, and the generalization ability of the models is assessed using a special dataset consisting of 400 images of difficult-to-identify plant disease cases. |
| format | Article |
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| institution | Kabale University |
| issn | 2079-7737 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Biology |
| spelling | doaj-art-c53236592ae44caa9d3d16ee3f6f3dde2025-01-24T13:23:39ZengMDPI AGBiology2079-77372025-01-011419910.3390/biology14010099Evaluation of Different Few-Shot Learning Methods in the Plant Disease Classification DomainAlexander Uzhinskiy0Joint Institute for Nuclear Research, 6 Joliot-Curie, Dubna 141980, RussiaEarly detection of plant diseases is crucial for agro-holdings, farmers, and smallholders. Various neural network architectures and training methods have been employed to identify optimal solutions for plant disease classification. However, research applying one-shot or few-shot learning approaches, based on similarity determination, to the plantdisease classification domain remains limited. This study evaluates different loss functions used in similarity learning, including Contrastive, Triplet, Quadruplet, SphereFace, CosFace, and ArcFace, alongside various backbone networks, such as MobileNet, EfficientNet, ConvNeXt, and ResNeXt. Custom datasets of real-life images, comprising over 4000 samples across 68 classes of plant diseases, pests, and their effects, were utilized. The experiments evaluate standard transfer learning approaches alongside similarity learning methods based on two classes of loss function. Results demonstrate the superiority of cosine-based methods over Siamese networks in embedding extraction for disease classification. Effective approaches for model organization and training are determined. Additionally, the impact of data normalization is tested, and the generalization ability of the models is assessed using a special dataset consisting of 400 images of difficult-to-identify plant disease cases.https://www.mdpi.com/2079-7737/14/1/99plant disease classificationdeep learningfew-shot learningone-shot learningSiamese networksangular margin-based loss function |
| spellingShingle | Alexander Uzhinskiy Evaluation of Different Few-Shot Learning Methods in the Plant Disease Classification Domain Biology plant disease classification deep learning few-shot learning one-shot learning Siamese networks angular margin-based loss function |
| title | Evaluation of Different Few-Shot Learning Methods in the Plant Disease Classification Domain |
| title_full | Evaluation of Different Few-Shot Learning Methods in the Plant Disease Classification Domain |
| title_fullStr | Evaluation of Different Few-Shot Learning Methods in the Plant Disease Classification Domain |
| title_full_unstemmed | Evaluation of Different Few-Shot Learning Methods in the Plant Disease Classification Domain |
| title_short | Evaluation of Different Few-Shot Learning Methods in the Plant Disease Classification Domain |
| title_sort | evaluation of different few shot learning methods in the plant disease classification domain |
| topic | plant disease classification deep learning few-shot learning one-shot learning Siamese networks angular margin-based loss function |
| url | https://www.mdpi.com/2079-7737/14/1/99 |
| work_keys_str_mv | AT alexanderuzhinskiy evaluationofdifferentfewshotlearningmethodsintheplantdiseaseclassificationdomain |